NISTIR 7761 Rev. 1 

NIST

Smart Grid Interoperability Panel 

Priority Action Plan 2: 

Guidelines for Assessing Wireless 

Standards for Smart Grid 

Applications 

Participants of the Priority Action Plan 2 working group 

David Cypher (editor) 
NISTIR 7761 Rev. 1 

NIST

Smart Grid Interoperability Panel 

Priority Action Plan 2: 

Guidelines for Assessing Wireless 

Standards for Smart Grid 

Applications 

Participants of the Priority Action Plan 2 working group 

David Cypher (editor) 

Advanced Network Technologies Division 

Information Technology Laboratory (ITL) 

June 2014 

U.S. Department of Commerce 

Penny Pritzker, Secretary 

National Institute of Standards and Technology 

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director 

 
i 

TABLE OF CONTENTS 

PREFACE ............................................................................................................................................. 1

AUTHORS ........................................................................................................................................... 2

1 OVERVIEW OF THE PROCESS ....................................................................................................... 3

2 ACRONYMS AND DEFINITIONS .................................................................................................... 4

2.1 ACRONYMS .......................................................................................................................................... 4

2.2 DEFINITIONS ........................................................................................................................................ 7

3 SMART GRID CONCEPTUAL MODEL AND BUSINESS FUNCTIONAL REQUIREMENTS ...................... 13

3.1 SMART GRID CONCEPTUAL REFERENCE DIAGRAMS .................................................................................... 13

3.2 LIST OF ACTORS .................................................................................................................................. 17

3.3 SMART GRID USE CASES ....................................................................................................................... 19

3.4 SMART GRID BUSINESS FUNCTIONAL AND VOLUMETRIC REQUIREMENTS........................................................ 21

3.5 USE OF SMART GRID USER APPLICATIONS’ QUANTITATIVE REQUIREMENTS FOR PAP02 TASKS .......................... 24

3.6 SECURITY ........................................................................................................................................... 24

4 WIRELESS TECHNOLOGY ........................................................................................................... 25

4.1 TECHNOLOGY DESCRIPTOR HEADINGS ..................................................................................................... 25

4.2 TECHNOLOGY DESCRIPTOR DETAILS ........................................................................................................ 26

4.2.1 Descriptions of Groups 1-7 Submissions ............................................................................... 26

4.2.1.1 Group 1: Applicable Smart Grid Communications Sub-Network(s) .............................................. 27

4.2.1.2 Group 2: Data / Media Type Supported .......................................................................................... 29

4.2.1.3 Group 3: Range Capability (or Coverage Area When Applicable).................................................. 29

4.2.1.4 Group 4: Mobility .............................................................................................................................. 30

4.2.1.5 Group 5: Channel / Sector Data Rates and Average Spectral Efficiency ....................................... 30

4.2.1.6 Group 6: Spectrum Utilization .......................................................................................................... 35

4.2.1.7 Group 7: Data Frames, Packetization, and Broadcast Support ...................................................... 36

4.2.2 Descriptions of Groups 8-12 Submissions ............................................................................. 37

4.2.2.1 Group 8: Link Quality Optimization ................................................................................................. 38

4.2.2.2 Group 9: Radio Performance Measurement and Management .................................................... 39

4.2.2.3 Group 10: Power Management........................................................................................................ 39

4.2.2.4 Group 11: Connection Topologies ................................................................................................... 39

4.2.2.5 Group 12: Connection Management ............................................................................................... 40

4.2.3 Descriptions of Groups 13-17 Submissions ........................................................................... 41

4.2.3.1 Group 13: QoS and Traffic Prioritization ......................................................................................... 42

4.2.3.2 Group 14: Location Based Technologies ......................................................................................... 43

4.2.3.3 Group 15: Security and Security Management ............................................................................... 43

4.2.3.4 Group 16: Unique Device Identification .......................................................................................... 43

4.2.3.5 Group 17: Technology Specification Source.................................................................................... 43

4.2.4 Group 18: Wireless Functionality not Specified by Standards .............................................. 43

4.3 WIRELESS TECHNOLOGY / STANDARD SUBMISSIONS .................................................................................. 45

5 MODELING AND EVALUATION APPROACH ................................................................................ 47

5.1 ASSESSMENT OF WIRELESS TECHNOLOGIES WITH RESPECT TO SMART GRID REQUIREMENTS .............................. 47

5.1.1 Initial Screening .................................................................................................................... 47

5.1.2 Refinements to Initial Screening ........................................................................................... 48

5.1.2.1 Mathematical Models ....................................................................................................................... 48

5.1.2.2 Simulation Models ............................................................................................................................ 48

5.1.2.3 Testbeds ............................................................................................................................................. 48

5.1.2.4 Network Design ................................................................................................................................. 48

5.2 MODELING FRAMEWORK ...................................................................................................................... 49
ii 

5.2.1 Channel Propagation or Path Loss Models ........................................................................... 50

5.2.1.1 Generic Path Loss Model .................................................................................................................. 51

5.2.1.2 Indoor Path Loss Models .................................................................................................................. 52

5.2.1.2.1 ITU-R M.1225 Indoor Model ....................................................................................................... 53

5.2.1.2.2 WINNER II Indoor Model ............................................................................................................. 54

5.2.1.2.3 ITU-R M.2135-1 Indoor Model .................................................................................................... 55

5.2.1.2.4 NIST PAP02-Task 6 Model ........................................................................................................... 55

5.2.1.2.5 Indoor Model Comparison .......................................................................................................... 56

5.2.1.2.6 Modeling Floor-to-Floor Penetration Losses in Multilevel Buildings ....................................... 57

5.2.1.2.7 Indoor Model Summary .............................................................................................................. 58

5.2.1.3 Large Scale Outdoor Path Loss Models ........................................................................................... 59

5.2.1.3.1 Hata-Okumura Model ................................................................................................................. 60

5.2.1.3.2 COST231-Hata aka Modified Hata Model .................................................................................. 61

5.2.1.3.3 WINNER II Model ......................................................................................................................... 62

5.2.1.3.4 ITU-R M.2135-1 Model ................................................................................................................ 63

5.2.1.3.5 Erceg-Stanford University Interim (SUI) Model ......................................................................... 65

5.2.1.3.6 Comparing Large Scale Path Loss Models to Smart Grid Requirements ................................. 66

5.2.1.3.7 Path Loss Due to Foliage ............................................................................................................. 67

5.2.1.3.8 Path Loss Due to Path Obstructions ........................................................................................... 69

5.2.1.3.9 Modified Erceg-SUI Model .......................................................................................................... 71

5.2.1.3.10 Model Limitations with Respect to Meeting Smart Grid Deployment Requirements............ 73

5.2.1.3.11 Modeling Extreme Terrain Characteristics ................................................................................ 74

5.2.1.4 Atmospheric Absorption ................................................................................................................... 74

5.2.1.5 Line of Sight (LoS) and Fresnel Zone Clearance .............................................................................. 75

5.2.2 Range and Coverage Analysis ............................................................................................... 77

5.2.2.1 Link Budget ........................................................................................................................................ 77

5.2.2.1.1 Fade Margins................................................................................................................................ 81

5.2.2.1.2 Interference Margin .................................................................................................................... 84

5.2.2.1.3 Penetration Loss .......................................................................................................................... 85

5.2.2.2 Deployment Trade-offs ..................................................................................................................... 86

5.2.2.2.1 BS and SS Antenna Heights ......................................................................................................... 86

5.2.2.2.2 Impact of Spectrum Choices ....................................................................................................... 92

5.2.3 Estimating Channel and BS Sector Capacity ......................................................................... 94

5.2.4 Physical Layer (PHY) Model................................................................................................. 100

5.2.5 MAC Sublayer Model .......................................................................................................... 101

5.2.6 Multi-Hop (or Multi-Link) Model ......................................................................................... 102

5.2.7 Modeling Latency ............................................................................................................... 103

5.2.7.1 Binomial Distribution Model .......................................................................................................... 104

5.2.7.2 M/D/1 and M/M/1 Queuing System Models ................................................................................ 108

5.2.7.3 Comparing the Binomial Distribution Model with the M/M/1 Model ........................................ 112

5.2.7.4 Additional Latency Considerations and Conclusions .................................................................... 113

6 PRACTICAL CONSIDERATIONS IN THE DEPLOYMENT OF WIRELESS NETWORKS FOR SG 

APPLICATIONS ................................................................................................................................ 116

6.1 COVERAGE, CAPACITY, LATENCY TRADE-OFFS ......................................................................................... 116

6.1.1 Demographic Breakdown ................................................................................................... 117

6.1.2 Dense Urban Regions .......................................................................................................... 119

6.1.2.1 Dense Urban Latency Considerations ............................................................................................ 121

6.1.2.2 Relating Channel Capacity and Latency ......................................................................................... 121

6.1.3 Low Density Rural ............................................................................................................... 123

6.1.4 Summary ............................................................................................................................. 126

6.2 ADVANCED ANTENNA SYSTEMS AND SPECTRUM CONSIDERATIONS ............................................................. 127

6.2.1 Multiple Input Multiple Output (MIMO) Antennas ............................................................. 127

6.2.2 Beamforming and Beam steering ....................................................................................... 128

6.2.3 Practical Considerations and Spectrum Trade-offs ............................................................. 129

6.3 MULTI-LINK / MULTI-HOP / MESH TOPOLOGIES .................................................................................... 132
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6.3.1 Network Topology Revisited ............................................................................................... 134

6.3.2 The Neighborhood Area Network (NAN) in a Larger Context ............................................. 136

6.3.3 Other Neighbor Area Network (NAN) Topologies ............................................................... 138

6.3.4 NAN Network Components ................................................................................................. 139

6.3.5 Characteristics of NAN Multi-Hop Networks ...................................................................... 140

6.3.6 Additional Technical Characteristics of the NAN ................................................................ 140

6.3.7 Further Considerations for NAN Design and Routing ......................................................... 142

6.3.8 Smart Grid Neighborhood Area vs. Wide Area Networks ................................................... 144

6.4 ADDRESSING THE CHALLENGES WITH MULTI-TENANT HIGH RISE BUILDINGS ................................................. 147

6.4.1 An Alternative Wireless Approach ...................................................................................... 147

6.4.2 Conclusion ........................................................................................................................... 149

6.5 SG FRAMEWORK AND WIRELESS MODELING TOOL .................................................................................. 150

6.5.1 Modeling tool input parameters ......................................................................................... 151

6.5.2 Modeling tool outputs ........................................................................................................ 155

6.5.3 Limitations of the modeling tool ......................................................................................... 156

6.6 INTEROPERATING AND INTERWORKING WITH OTHER WIRELESS TECHNOLOGIES ............................................. 158

6.6.1 Satellite Communication Networks .................................................................................... 158

6.6.2 Smart Grid Solutions in the 450 MHz to 470 MHz IG Band ................................................. 160

6.6.2.1 Frequency Usage and Capabilities ................................................................................................. 160

6.6.2.2 Implementation Using the IG Band................................................................................................ 161

6.6.2.3 IG Band Summary ............................................................................................................................ 162

6.7 ASSESSMENT OF MODELING TOOL RESULTS ........................................................................................... 162

6.7.1 Impact of Packet Size .......................................................................................................... 163

6.7.2 Summary of Terrestrial-based Technology Submissions ..................................................... 166

6.7.3 Baseline Parameter Choices for Modeling Tool Assessment .............................................. 167

6.7.4 Wireless Technology Assessment ....................................................................................... 170

6.7.4.1 Meeting Highload Demand ............................................................................................................ 171

6.7.4.2 Meeting Baseload Demand ............................................................................................................ 175

6.7.4.3 Wireless Assessments for the ISM bands in a PMP Topology ..................................................... 176

6.7.4.4 Meeting Latency Requirements ..................................................................................................... 177

6.7.4.5 Sensitivity Analysis .......................................................................................................................... 180

6.7.5 Assessment results summary .............................................................................................. 186

6.8 CROSS WIRELESS TECHNOLOGY CONSIDERATIONS ................................................................................... 191

7 CONCLUSIONS ....................................................................................................................... 195

8 BIBLIOGRAPHY ....................................................................................................................... 196

 
iv 

 
1 

Preface 

Wireless technologies for technical and business communications have been available for 

over a century and are widely used for many popular applications. The use of wireless 

technologies in the power system is also not new. Its use for system monitoring, 

metering and data gathering goes back several decades. However, the advanced 

applications and widespread use now foreseen for the smart grid require highly reliable, 

secure, well designed, and managed communication networks. 

The decision to apply wireless technologies for any given set of applications is a local 

decision that must take into account several important elements including both technical 

and business considerations. Smart grid applications requirements must be defined with 

enough specification to quantitatively define communications traffic loads, levels of 

performance, and quality of service. Applications requirements must be combined with a 

complete set of management and security requirements for the life-cycle of the system. 

These requirements can then be used to assess the suitability of various wireless 

technologies to meet the requirements in the particular applications environment. 

This report contains key tools and methods to assist smart grid system designers in 

making informed decisions about existing and emerging wireless technologies. An 

initial set of quantified requirements have been brought together for advanced metering 

infrastructure (AMI) and initial distribution automation (DA) communications. These 

two areas present technological challenges due to their scope and scale. These systems 

will span widely diverse geographic areas and operating environments and population 

densities ranging from urban to rural. 

The wireless technologies presented here encompass different technologies that range in 

capabilities, cost, and ability to meet different requirements for advanced power systems 

applications. System designers are further assisted by the presentation of a set of wireless 

functionality and characteristics captured in a matrix for existing and emerging standards 

based wireless technologies. Details of the capabilities are presented in this report as a 

way for designers to initially sort through the available wireless technology options. 

To further assist decision making, the report presents a set of tools in the form of models 

that can be used for parametric analyses of the various wireless technologies. 

This report represents an initial set of guidelines to assist smart grid designers and 

developers in their independent evaluation of candidate wireless technologies. While 

wireless holds many promises for the future, it is not without limitations. In addition 

wireless technology continues to evolve. Priority Action Plan 2 (PAP02) fundamentally 

cuts across the entire landscape of the smart grid. Wireless is one of several 

communications options for the smart grid that must be approached with technical rigor 

to ensure communication systems investments are well suited to meet the needs of the 

smart grid both today, as well as in the future. 

The scope and scale of wireless technology will represent a significant capital 

investment. In addition the smart grid will be supporting a wide diversity of applications 
2 

including several functions that represent critical infrastructure for the operation of the 

nation’s electric and energy services delivery systems. 

Authors 

Steve Barclay – ATIS 

Ron Cunningham – American Electric Power 

David Cypher – NIST 

Matthew Gillmore – Itron 

William (Bill) Godwin – Duke Energy 

Nada Golmie – NIST 

Doug Gray – WiMAX Forum 

Jorjeta Jetcheva – Itron 

Farrokh Khatibi - Qualcomm 

Bruce Kraemer – Marvell 

Orlett W. Pearson – TIA TR-45.5 

Thomas Hurley - NXEGEN LLC 

Anthony Noerpel - Hughes 

John Geiger – GE Energy Services/Digital Energy/Energy Management 
3 

1 Overview of the Process 

The main objectives for release 2 of the NISTIR 7761 were: 

• Improve the intended interpretation and input of data from the Standards 

Development Organizations (SDOs) and alliances on the Wireless Functionality 

and Characteristic Matrix for the Identification of Smart Grid Domain 

Applications (section 4) 

• Revise how the previously included technology appendix content is addressed in 

section 4 and section 5 

• Provide more guidance to the reader on the use of various wireless standards and 

representative technologies for designers of the wireless telecommunication 

networks for smart grid deployments (see section 5 and section 6) 

• Incorporate an extension to the modeling and evaluation approach via a 

framework (see section 6.5), that: 

o fully exploits the smart grid requirements from the UCA International 

Users Group (UCAIug) – Open Smart Grid User’s Group (OpenSGug) - 

SG Communications Working Group - SG-Network Task Force via a 

modeled deployment scenario as input; 

o develops an Smart Grid (SG) framework and wireless modeling tool 

(spreadsheet) that incorporates inputs from section 4, Wireless 

Functionality and Characteristics Matrix and representative technology 

operating parameters; 

o outputs the quantities of network gear calculated across several spectrum 

bands and wireless end-point density and terrain and clutter categories. 

• Provide sensitivity analysis and impacts around many of the input parameters and 

provide guidance (see section 5 and section 6) 

These objectives were addressed by various tasks and working documents identified as 

release or version 2 artifacts located in the Smart Grip Interoperability Panel (SGIP) 

Priority Action Plan 2 (PAP02), first link below and SGIP 2.0 PAP02 the second link 

below: 

 http://collaborate.nist.gov/twiki-sggrid/bin/view/SmartGrid/PAP02Wireless

 http://members.sgip.org/apps/org/workgroup/sgip-pap02wg/ 
4 

2 Acronyms and Definitions 

The acronyms and definitions provided are used in this report and in some of its 

referenced supporting documentation. 

2.1 Acronyms 

AMI Advanced Metering Infrastructure 

AP Access Point 

ARQ Automatic Repeat-reQuest 

BE Best Effort 

BER Bit Error Rate 

BGAN Broadband Global Area Network 

BPSK Binary Phase Shift Keying 

BS Base Station 

BW Bandwidth 

CCI Co-Channel Interference 

CIA Confidentiality, Integrity, and Availability 

CIS Customer Information Service 

COST CO-operative for Scientific and Technical research 

DA Distribution Automation 

DAC Distributed Application Controller 

DAP Data Aggregation Point 

DB Database 

DER Distributed Energy Resources 

DL Downlink 

DMS Distribution Management System 

DRMS Distribution Resource Management System 

DSDR Distribution Systems Demand Response 

DSM Demand Side Management 

EDGE Enhanced Data Rates for GSM Evolution 

EIRP Effective Isotropic Radiated Power 

EMS Energy Management System 

EP End-point 

ESI Energy Services Interface 

EUMD End Use Measurement Device 

EV/PHEV Electric Vehicle/Plug-in Hybrid Electric Vehicle 

EVSE Electric Vehicle Service Element 

FAN Field Area Network 

FCC Federal Communications Commission 

FDD Frequency Division Duplexing 

FEC Forward Error Correction 

FEP Front End Processor 
5 

FER Frame Error Rate 

FERC Federal Energy Regulatory Commission1

FSK Frequency Shift Keying 

FTP File Transfer Protocol 

G&T Generations and Transmission 

GBR Guaranteed Bit Rate 

GIS Geographic Information System 

GL General Ledger 

GMR Geo Mobile Radio 

GPRS General Packet Radio Service 

GPS Global Positioning System 

GSM Global System for Mobile Communications 

HAN Home Area Network 

HARQ Hybrid Automatic Repeat reQuest 

HRPD High Rate Packet Data 

HSPA+ Evolved High-Speed Packet Access 

HVAC Heating, Ventilating, and Air Conditioning 

H-FDD Half-duplex Frequency Division Duplexing 

IKB Interoperability Knowledge Base 

IP Internet Protocol 

ISM Industrial Scientific and Medical 

ISO Independent System Operator 

ITU International Telecommunications Union 

LB Link Budget 

LMS Load Management System 

LMS/DRMS Load Management System/ Distribution Resource 

Management System 

LoS Line of Sight 

LTE Long Term Evolution 

LV Low Voltage 

MAC Medium Access Control 

MBR Maximum Bit Rate 

MCS Modulation and Coding Scheme 

MDMS Meter Data Management System 

MIMO Multiple-Input / Multiple-Output 

MS Mobile Station 

MSS Mobile Satellite Services 

MU-MIMO Multi-User Multiple Input Multiple Output (Antennas) 

MV Medium Voltage 

NAN Neighborhood Area Network 

NISTIR NIST Interagency Report 

NMS Network Management System 

 

1

 Federal Energy Regulatory Commission - www.ferc.gov 
6 

ODW Operational Data Warehouse 

OFDMA Orthogonal Frequency Division Multiple Access

OH Overhead 

OMS Outage Management System 

OSI Open Systems Interconnection 

OTA Over-the-Air 

PAP Priority Action Plan 

PCT Programmable Communicating Thermostat 

PHEV Plug-in Hybrid Electric Vehicle 

PHY Physical Layer 

PL Path Loss 

PMP Point-to-Multipoint 

PtP Point-to-Point 

QAM Quadrature Amplitude Modulation 

QoS Quality of Service 

QPSK Quadrature Phase Shift Keying 

REP Retail Electric Provider 

RF Radio Frequency 

RTO Regional Transmission Operator 

RTU Remote Terminal Unit 

Rx Receiver or receiving 

SCADA Supervisory Control and Data Acquisition 

SDO Standards Development Organization 

SE Spectral Efficiency 

SER Symbol Error Rate 

SGIP Smart Grid Interoperability Panel 

SINR Signal to Interference plus Noise Ratio 

SM Smart Meter 

SNR Signal to Noise Ratio 

SRS System Requirements Specification 

SS Subscriber Station 

SUI Stanford University Interim 

TCP Transmission Control Protocol 

TDD Time Division Duplexing 

TF Task Force 

Tx Transmitter or Transmitting 

UL Uplink 

VAR Volt-Amperes Reactive 

VoIP Voice over Internet Protocol 

VVWS Volt-VAR-Watt System 

WAMS Wide-Area Measurement System 

WAN Wide Area Network 

WLAN Wireless Local Area Network 
7 

2.2 Definitions2

Access Point A stationary node, consisting of a transmitter and receiver, used to 

aggregate traffic in a wireless network. This term is most often used to 

describe this functionality for indoor wireless local area networks, but 

sometimes also used for outdoor terrestrial local area networks. Also 

see Base Station. 

Actor A generic name for devices, systems, or programs that make decisions 

and exchange information necessary for performing applications: smart 

meters, solar generators, and control systems represent examples of 

devices and systems. 

Advanced Metering 

Infrastructure 

A network system specifically designed to support two-way 

connectivity to Electric, Gas, and Water meters or more specifically for 

AMI meters and potentially the Energy Service Interface for the Utility 

(or ESI-Utility). 

Aggregation Practice of summarizing certain data and presenting it as a total without 

any personally identifiable information identifiers

Aggregator SEE FERC OPERATION MODEL 

Applications Tasks performed by one or more actors within a domain. 

Asset Management 

System 

A system(s) of record for assets managed in the smart grid. 

management context may change (e.g., financial, network). 

Backhaul The portion of the network that comprises the intermediate links between the 

core network or backbone network and the sub-networks at the edge of a 

hierarchical network.

Base Station A stationary node used to aggregate and backhaul traffic in a terrestrial 

multi-cellular wireless network. In an AMI network or NAN, the DAP 

serves the same function as a Base Station. 

Capacitor Bank This is a device used to add capacitance as needed at strategic points in 

a distribution grid to better control and manage volt-amperes reactive 

(VARs) and thus the power factor and they will also affect voltage 

levels. 

Capacity-Limited 

(Deployment) 

A wireless cellular-like deployment for which the number of base 

stations is determined by the capacity requirements of the geographic 

area. (may also be referred to as capacity-constrained) 

Cell Generally used to describe a base station and its surrounding coverage 

area. 

Cell Site Refers to the geographical position for a base station 

Client Device Used to describe customer or end user equipment. Device can be 

mobile, portable, or stationary (fixed). 

 

2

 The definitions are specific to this report’s context and intended usage. Even though other Standards 

Development Organizations have their own copyrighted definitions for some of these same terms, a 

specific effort to harmonize or obtain permission to reuse copyrighted definitions was not included in scope 

of this work. 
8 

Common Web Portal Web interface for regional transmission operator, customers, retail 

electric providers and transmission distribution service provider to 

function as a clearing house for energy information. Commonly used in 

deregulated markets. 

Data Aggregation 

Point 

This device is a logical actor that represents a transition in most 

advanced metering infrastructure (AMI) networks between wide area 

networks and neighborhood area networks. (e.g., collector, cell relay, 

base station, access point, etc.) 

Data Collector See Substation controller 

Demand Side 

Management 

A system that co-ordinates demand response / load shedding messages 

indirectly to devices (e.g., set point adjustment) 

Distribution 

Management System 

A system that monitors, manages and controls the electric distribution 

system. 

Distribution Systems 

Demand Response 

A system used to reduce load during peak demand. Strictly used for 

distribution systems only. 

Downlink (or 

Downstream) 

Data traffic flow in the network from the Operations Center towards the 

end-point. 

Electric Vehicle 

/Plug-in Hybrid 

Electric Vehicle 

Cars or other vehicles that draw electricity from batteries to power an 

electric motor. PHEVs also contain an internal combustion engine. 

End User (End-User 

Node) 

Same as client device, terminal, etc. 

End-Point Term used to describe termination points in a NAN or AMI network. 

Energy Services 

Interface 

Provides the communications interface to the utility. It provides 

security and, often, coordination functions that enable secure 

interactions between relevant home area network devices and the utility. 

Permits applications such as remote load control, monitoring and 

control of distributed generation, in-home display of customer usage, 

reading of non-energy meters, and integration with building 

management systems. Also provides auditing / logging functions that 

record transactions to and from home area networking devices. 

Enterprise Bus The enterprise bus consists of a software architecture used to construct 

integration services for complex event-driven and standards-based 

messaging to exchange meter or grid data. The enterprise bus is not 

limited to a specific tool set; rather, it is a defined set of integration 

services. 

Fault Detector A device used to sense a fault condition and can be used to provide an 

indication of the fault. 

Field Area Network A network designed to provide connectivity to field DA devices. The 

FAN may provide a connectivity path back to the substation upstream of 

the field DA devices or connectivity that bypasses the Substations and 

links the field DA devices into a centralized management and control 

system (commonly called a SCADA system). 

Field Force Employee working in the service territory that may be working with 

smart grid devices. 
9 

Frame A fixed length digital data transmission unit that includes 

synchronization at the link layer (layer 2). A frame will carry one or 

more packets of varied length. (also see packet) 

Frequency Reuse 

Factor 

A term to describe how often a channel is reused with a base station or 

cell. For example for a 3-sector cell, a Frequency Reuse Factor of 1 

indicates the same channel is reused in each of the 3 sectors. Reuse 3 

indicates that a different channel is used in each of the 3 sectors, 

Goodput Goodput is the application level throughput, i.e., the number of useful 

bits per unit of time forwarded by the network from a certain source to a 

certain destination, excluding protocol overhead, and excluding 

retransmitted data packets. 

Header The portion of a packet, before the data field that typically contains 

source and destination addresses, control fields and error check fields. 

Home Area Network A network of energy management devices, digital consumer electronics, 

signal-controlled or enabled appliances, and applications within a home 

environment that is on the home side of the electric meter. 

Latency As used in the OpenSG – SG Communications SG-Network TF’s 

Requirement Table, is the summation of actor (including network 

nodes) processing time and network transport time measured from an 

actor sending or forwarding a payload to an actor, and that receiving 

actor processing (consuming) the payload. This latency is not the 

classic round trip response time, or the same as network link latency. 

Latency-Limited 

(Deployment) 

A wireless cellular-like deployment in which the number of base 

stations is determined by the number of end-points and payloads that 

can be supported by each base station while meeting a specific payload 

latency requirement. 

Link Budget Accounts for the attenuation of the transmitted signal due to antenna 

gains, propagation, and miscellaneous losses. 

Load Management 

System 

A system that controls load by sending messages directly to device (e.g., 

On / Off) 

M/D/1 and M/M/1 M/D/1 describes a queuing system model with a Poisson arrival process, 

a deterministic service rate distribution, and a single server. In the 

notation, M = Markov or Markovian, D=Deterministic, and 1 indicates 

the number of servers. 

M/M/1 describes a queuing system model with a Poisson arrival process 

for which the service time is exponentially distributed rather than 

deterministic. 

Macro-cell A base station in a cellular architecture with a large coverage area, 

typically limited only by the propagation conditions and system gain. 

Mega-cell A point-to-multipoint cell designed to provide connectivity over an 

extremely large geographical area. Satellite coverage is typical. 

Micro-cell A base station in a cellular architecture with a coverage area greater than 

a pico-cell but less than a macro-cell 

Mobile Station See client device 
10 

Multi-Hop 

(Topology) 

A group of interconnected nodes in a common network infrastructure 

where communication links can be established via node-to-node or hopto-hop links, similar to relay functionality. 

Multi-Link 

(Topology) 

An interconnection of multiple discrete networks, such as linking a 

HAN with a NAN, then to a WAN. 

Multi-User MIMO A technique used with multiple antenna systems in which, transmissions 

from multiple end-users are aggregated on a single channel at the 

receiver by using multiple receive antennas. 

Neighborhood Area 

Network 

A network system intended to provide direct connectivity with Smart 

Grid end devices in a relatively small geographic area. In practice a 

NAN may encompass an area the size of a few blocks in an urban 

environment, or areas several miles across in a rural environment. 

Net Spectral 

Efficiency 

The channel spectral efficiency at the application layer taking into 

account all channel overhead factors including encryption. (= goodput ÷ 

channel BW) 

Network 

Management System 

A system that manages fault, configuration, auditing/accounting, 

performance and security of the communication. This system is 

exclusive from the electrical network. 

Outage Management 

System 

A system that receives out power system outage notifications and 

correlates where the power outage occurred 

Packet The unit of data that is routed from a source to a destination on a packetswitched network. The packet includes a header, footer, and other 

overhead bits along with the message ‘payload’. Packets do not 

generally have a fixed size. 

Payload The actual message data carried within a packet. From a business 

application payload perspective, application payload is the totality of the 

business data for an asymmetric message that the telecommunications 

standard and implementing technology may need to segment into 

multiple packets from which only a portion of the business application 

payload is included. 

Pico-cell A base station coverage area within a cellular network designed to cover 

a very small area for extending range in difficult coverage areas or to 

add capacity in a high density area. 

Power Factor A dimensionless quantity that relates to efficiency of the electrical 

delivery system for delivering real power to the load. Numerically, it is 

the cosine of the phase angle between the voltage and current 

waveforms. The closer the power factor is to unity the better the 

inductive and capacitive elements of the circuit are balanced and the 

more efficient the system is for delivering real power to the load(s). 

Programmable 

Communicating 

Thermostat 

A device within the premise that has communication capabilities and 

controls heating, ventilation and cooling systems. 

Range-Limited 

(Deployment) 

A wireless cellular-like deployment for which the number of base 

stations to cover the area of interest is determined strictly by the linkbudget and path loss. (may also be referred to as range-constrained) 
11 

Rate Adaptation The mechanism by which a modem adjusts its modulation scheme, 

encoding and/or speed in order to reliably transfer data across channel 

exhibiting different signal to noise ratio (SNR) characteristics. 

Recloser A device used to sense fault conditions on a distribution line and trip 

open to provide protection. It is typically programmed to automatically 

close (re-close) after a period of time to test if the fault has cleared. 

Two general types of reclosers are typically deployed e.g., non-teamed 

and teamed. 

• Non-Teamed – After several attempts of reclosing it can be 

programmed to trip open and stop trying to reclose until reset 

either locally or under remote control. 

• Teamed - A device that can sense fault conditions on a 

distribution line and to communicate with other related reclosers 

(the team) to sectionalize the fault and provide a coordinated 

open / close arrangement to minimize the effect of the fault. 

Regional 

Transmission 

Operator 

An organization that is established with the purpose of promoting 

efficiency and reliability in the operation and planning of the electric 

transmission grid and ensuring non-discrimination in the provision of 

electric transmission services based on the following required / 

demonstrable characteristics and functions. 

Remote Terminal 

Unit 

Aggregator of multiple serialized devices to a common communications 

interface 

Smart Meter Term applied to a Two-Way Meter (meter metrology plus a network 

interface component) with included energy services interface (ESI) in 

the meter component 

Spatial Diversity A technique employed with multiple antenna systems to increase link 

availability or link budget in which each uncorrelated Tx antenna 

transmits the same data stream. 

Spatial Multiplexing A technique employed with multiple antenna systems to increase peak 

and average channel capacity and spectral efficiency in which each 

uncorrelated Tx antenna transmits a different data stream. 

Sub Meter Premise based meter (e.g., used for Distributed Energy Resources and 

PHEV), which permits additional metering capabilities subordinate to a 

main meter. 

Sub-Network A self-contained wireless or wire-line domain, use case, or area-focused 

network within the overall SG Network System 

Subscriber Station See client device 

Substation Controller Distributed processing device that has supervisory control or 

coordinates information exchanges from devices within a substation 

from a head end system. 

Switch A device under remote control that can be used to open or close a circuit 

Terminal See client device 

Throughput The number of bits (regardless of purpose) moving over a 

communications link per unit of time. Throughput is most commonly 

expressed in bits per second (b/s). 
12 

Transformer (MV-toLV) A standard point of delivery transformer. In the smart grid context it is 

assumed there will be a need to measure some electrical or physical 

characteristics of this transformer such as voltage (high and/or low side) 

current, MV load, temperature, etc. 

Universal Frequency 

Reuse 

Same as Frequency Reuse factor of 1 

Uplink (or Upstream) Defines data traffic flowing in the SG network in the direction towards 

the Operation Center. 

Use Case A systems engineering tool for defining a system’s behavior from the 

perspective of users. In effect, a use case is a story told in structure and 

detailed steps—scenarios for specifying required usages of a system, 

including how a component, subsystem, or system should respond to a 

request that originates elsewhere. 

Voltage Regulator This device is in effect an adjustable ratio transformer positioned at 

strategic points in a distribution grid and is utilized to better manage and 

control the voltage as it changes along the distribution feeder. 

Voltage Sensor A device used to measure and report electrical properties (such as 

voltage, current, phase angle or power factor, etc.) for specific voltage 

levels, e.g., low voltage customer delivery point, medium voltage 

distribution line points. 

Volt-Amperes 

Reactive 

In an alternating current power system the voltage and current measured 

at a point along the delivery system will often be out of phase with each 

other as a result the combined effects of the resistive and reactive (i.e., 

the capacitance and inductive) characteristics of the delivery system 

components and the load. The phase angle difference at a point along 

the delivery system is an indication of how well the inductive and 

capacitive effects are balanced at that point. The real power passing that 

point is the product of the magnitude of the voltage and current and the 

cosine of the angle between the two. The VAR parameter is the product 

of the magnitude of the voltage and current and the sine of the angle 

between the two. The magnitude of the VAR parameter is an indication 

of the phase imbalance between the voltage and current waveforms. 

Web Portal Interface between customers and their smart grid service provider (e.g., 

utility or third party or both). 

 
13 

3 Smart Grid Conceptual Model and Business Functional Requirements 

This section provides an overview of the primary sets of information that UCAIug – 

OpenSG – SG Communications – SG-Network Task Force (SG-Network TF) prepared to 

address task 3 of PAP02, plus an explanation of how this information is intended to be 

interpreted and an example of how to consume the information as an input into other 

analysis tools (e.g., network traffic modeling). 

3.1 Smart Grid Conceptual Reference Diagrams 

SG-Network TF expanded upon the smart grid conceptual reference and framework 

diagrams that were introduced in the first release of NIST Special Publication 1108 - 

NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 1.0 

and other reference diagrams included in NISTIR 7628 - Guidelines for Smart Grid 

Cyber Security. The NIST Smart Grid Framework release diagram is shown in Figure 1, 

along with two views of SG-Network TF’s conceptual domain actors and interfaces 

reference diagrams, one without (Figure 2) and one with (Figure 3) cross domain data 

flows. Alternative (optional) interfaces between actors and communication paths 

amongst actors are also contained in the diagrams. These reference diagrams are further 

explained in smart grid use case documentation and detailed business functional and 

volumetric requirements in the sections that follow. In these three figures the customer 

domain includes: residential customers or commercial or industrial customers. 
14 

Figure 1 - Smart grid conceptual reference diagram – NIST Smart Grid Framework 

1.0 January 2010 
15 

Figure 2 - OpenSG SG-Network TF smart grid conceptual reference diagram 

PCTH

IPDH LCH

CEH

SAH

16B2

16B1

8

1B

1Aa

1Ab

TW

15

13

4A

DSWa

HDW

UI

20

CWPI

MeA

ESImH

ESIpH

ESIpPL

CLI

MsgPL

CEMSPL

DAPW

CEMSH

2Aa

2C

2Fa

2Ja

2Jb

2Fb

RI

2Da

2Ab

FA

FMe

30

11

7

19

29

14

9

12

6

BI

2Ha

2Ia

2G

2Db

RCW

UCW

WI

1Cba

DSWb

PW

18

NW

2Ib

2Hb

RTSN

TSF

DACsSN

RsSN

FGsSN

Field Area Network (j)

RDSN

RGF CBF

SF

SWF

RCF

CSF

CGF

DSSN

DGSN

4ECR

STF

WTS

WDS

21

22

FF

16

17

RDSF

WDFa

WDFb

23

24

25

26

ESIuH

ESIuA

FESIm

MgH

MwH

27

28

MgA

MwA

31

32

9B

8B

FMg

FMw

34

35

33

31b

CAI

36

38

39

38b

39b

FCB FS FST FSW

FRC

FCG

FCS

FRG
16 

Figure 3 - OpenSG_SG-Network TF smart grid conceptual reference diagram with 

cross domain data flows 
17 

The latest set of SG-Network TF reference diagrams are located at 

 http://osgug.ucaiug.org/UtiliComm/Shared%20Documents/Latest_Release_Deliv

erables/Diagrams/ 

3.2 List of Actors 

Table 1 maps the actors included in the SG-Network TF smart grid conceptual reference 

diagram (Figure 3) and the NIST smart grid conceptual reference diagram (Figure 1). 

The SG-Network TF high level list of actors are further qualified by domain and subdomain as used in documenting the smart grid business functional and volumetric 

requirements. Where there is no equivalent actor, a blank cell is used. 

Table 1: Mapping of actors to domain names 

SG-Network TF reference 

diagram descriptor (actor) 

SG-Network TF 

reference diagram 

domain name 

Related NIST release 1 

diagram descriptor 

(actor) 

Field Tools Customer / Distribution 

Generators Bulk Generation Generators 

Market Services Interface Bulk Generation Market Services Interface 

Plant Control Systems Bulk Generation Plant Control Systems 

 Customer Electric Storage 

Customer Energy Management 

System (EMS) 

Customer Customer EMS 

DERs (Solar, Wind, premise 

generation sources) 

Customer Distributed Generation 

ESI (3rd party) Customer Energy Services Interface 

ESI (Utility) Customer Energy Services Interface 

ESI (In meter) Customer Energy Services Interface 

Electric Vehicle Service 

Element (EVSE) / End Use 

Measurement Device (EUMD) 

Customer Customer Equipment 

Heating, Ventilating, and Air 

Conditioning (HVAC) 

Customer Customer Equipment 

IPD (In Premise Device) Customer Customer Equipment 

Load Control Device Customer Customer Equipment 

PCT Customer Thermostat 

PHEV Customer Electric Vehicle 

Phone / Email / Text / Web Customer Customer Equipment 

Smart Appliances Customer Appliances 

Smart Meter Customer Meter 

Sub-Meter Customer Customer Equipment 

Two Way Meter - Electric Customer Meter 

Two Way Meter - Gas Customer Meter 

Two Way Meter - Water Customer Meter 

Capacitor Bank Distribution Field Device 

Circuit Breaker Distribution Field Device 
18 

SG-Network TF reference 

diagram descriptor (actor) 

SG-Network TF 

reference diagram 

domain name 

Related NIST release 1 

diagram descriptor 

(actor) 

Recloser Distribution Field Device 

Distributed Customer 

Generation 

Distribution Distribution Generation 

Distributed Customer Storage Distribution Storage System 

Sectionalizer Distribution Field Device 

Switch Distribution Field Device 

Voltage Regulator Distribution Field Device 

Distributed Application 

Controller (DAC) 

Distribution / 

Transmission 

Substation Controller 

Distributed Generation Distribution / 

Transmission 

Distributed Generation 

Distributed Storage Distribution / 

Transmission 

Storage System 

Field Area Network (FAN) 

Gateway 

Distribution / 

Transmission 

Field Sensors Distribution / 

Transmission 

Field Device 

Remote Terminal Unit (RTU) Distribution / 

Transmission 

Data Collector 

Substation Devices Distribution / 

Transmission 

Substation Device 

Energy Market Clearinghouse Markets Energy Market

Clearinghouse 

Retailer / Wholesaler Markets Aggregator / Retail Energy 

Provider 

Regional Transmission 

Operator (RTO) / Independent 

System Operator (ISO) 

Markets RTO / ISO 

Aggregator Markets / Service 

Providers 

Aggregator 

 Operations Asset Mgmt 

 Operations WAMS 

AMI Head-End Operations Metering System 

Analytic Database Operations 

Certificate Authority Operations 

Distributed SCADA Front End 

Processor (FEP) 

Operations Distributed SCADA 

Demand Side Management 

(DSM) 

Operations Demand Response 

EMS Operations Utility EMS 

OMS Operations 
19 

SG-Network TF reference 

diagram descriptor (actor) 

SG-Network TF 

reference diagram 

domain name 

Related NIST release 1 

diagram descriptor 

(actor) 

Geographic Information 

System (GIS) 

Operations 

General Ledger (GL) / 

Accounts Payable / Receivable 

Operations 

Load Management System 

(LMS) 

Operations 

MDMS Operations MDMS 

NMS Operations 

RTO SCADA Operations RTO SCADA 

Security Key Manager Operations 

Transmission SCADA FEP Operations Transmission SCADA FEP 

Utility Distribution 

Management System (DMS) 

Operations DMS 

Utility EMS Operations EMS 

Work Management System Operations 

Bill Payment Organizations / 

Banks 

Service Provider Other 

Certificate Authority Service Provider 

Common Web PortalJurisdictional Service Provider Other 

Demand Side Management 

(DSM) 

Service Provider 

Home / Building Manager Service Provider Home / Building Manager 

Internet / Extranet Gateway Service Provider 

Load Management System 

(LMS) 

Service Provider 

ODW Service Provider 

REP CIS / Billing Service Provider Retail Energy Providers 

Billing 

REP CIS / Billing Service Provider Retail Energy Providers 

CIS 

Security Key Manager Service Provider 

Utility CIS / Billing Service Provider Utility CIS 

Utility CIS / Billing Service Provider Utility Billing 

Web Portal Service Provider 

3.3 Smart Grid Use Cases 

From the Interoperability Knowledge Base (IKB), 

 http://collaborate.nist.gov/twikisggrid/bin/view/SmartGrid/InteroperabilityKnowledgeBase#Use_Cases 
20 

Use cases come in many shapes and sizes. With respect to the IKB, fairly comprehensive 

use case descriptions are used to expose functional requirements for applications of the 

smart grid. In order to provide this depth, these use cases contain the following 

information: 

• Narrative: a description in prose of the application represented including all 

important details and participants described in the context of their 

activities 

• Actors: identification of all the persons, devices, subsystems, software 

applications that collaborate to make the use case work 

• Information Objects: defines the specific aggregates of information exchanged 

between actors to implement the use case 

• Activities / Services: description of the activities and services this use case relies 

on or implements 

• Contracts / Regulations: what contractual or regulatory constraints govern this 

use case 

• Steps: the step by step sequence of activities and messaging exchanges 

required to implement the use case 

For use cases following this description, see: 

 http://collaborate.nist.gov/twiki-sggrid/bin/view/SmartGrid/IKBUseCases 

SG-Network TF performed an exercise to research and to identify all pertinent use cases 

(namely concerning Advanced Metering Infrastructure (AMI) and Distribution 

Automation (DA)) that involve network communication to help satisfy the OpenSG input 

requirements into the NIST PAP02 tasks. Use cases from several sources (Southern 

California Edison, Grid Wise Architecture Console, Electric Power Research Institute 

and others) were researched. Table 2 summarizes the use cases SG-Network TF has 

currently in scope for this work effort. 

Table 2: OpenSG SG-Network TF use cases and status 

Smart grid use case3 – based on release V5.1.xls 

Customer Information / Messaging 

Demand Response – Direct Load Control (DR-DLC) 

Distributed Storage – Dispatch ; Island 

Distribution Systems Demand Response (DSDR) - 

Centralized Control 

Fault Clear Isolation Reconfigure (FCIR) – Distributed DAC 

– Substations; DMS; Regional Distributed DAC 

Field Distribution Automation Maintenance / Support – 

Centralized Control 

Meter Events 

Meter Read 

Outage Restoration Management 

 

3

 For several of the payloads that might be classified as associated to Accounting (Auditing), Fault 

Management, those payloads are included across several of the other listed use cases. 
21 

PHEV 

Premise Network Administration 

Pre-Pay Metering 

Pricing: 

Time of Use (TOU) / 

Real Time Pricing (RTP) / 

Critical Peak Pricing (CPP) 

Service Switch 

System Updates (Firmware / Program Update) 

Volt / VAR Management – Centralized Control 

Smart grid use case4 – potential for releases post V5.1.xls 

Configuration Management 

Distributed Generation 

Field Force Tools 

Performance Management 

Security Management 

Transmission automation support 

Documentation and description of the in-scope smart grid use cases by the SG-Network 

TF is contained in the System Requirements Specification (SRS) document [5]. The SGNetwork TF objective for the SRS is to provide sufficient information for the reader to 

understand the overall business requirements for a smart grid implementation and to 

summarize the business volumetric requirements at a use case payload level as focused 

on the communications networking requirements, without documenting the use cases to 

the full level of documentation detail as described by the IKB. 

The scope of the SRS focuses on explaining the objectives; the approach to documenting 

the use cases; inclusion of summarization of the network and volumetric requirements 

and necessary definition of terms; and guidance upon how to interpret and consume the 

business functional and volumetric requirements. The latest released version of the SRS 

is located at 

 http://osgug.ucaiug.org/UtiliComm/Shared%20Documents/Latest_Release_Deliv

erables/ 

with a file name syntax of “SG Network SRS Version vN Final.doc”, where N represents 

the version number. 

3.4 Smart Grid Business Functional and Volumetric Requirements 

There are many smart grid user applications (use cases) collections of documentation. 

Many have text describing the user applications (see IKB), but few contain quantitative 

business functional and volumetric requirements, which are necessary to design 

communications protocols, to assess, or to plan communication networks. Documenting 

the detailed actor to actor payloads and volumetric requirements allows for: 

 

4

 For the current status of what use cases and application payloads have been documented, see the latest 

Requirement Table (.xls) referenced in section 3.4.
22 

• aggregation of the details to various levels (e.g., specific interface or network link, 

a specific network or actor and have the supporting details versus making 

assumptions about those details) and 

• allows the consumer of the Requirements Table to scope and customize the smart 

grid deployment specific to their needs (e.g., which set of use cases, payloads, 

actors, communication path deployments). 

OpenSG -SG Communications - SG-Network TF took on the task to document the smart 

grid business functional and volumetric requirements for input into the NIST PAP02 

tasks and to help fill this requirements documentation void. The current SG-Network 

business functional and volumetric requirements are located at 

 http://osgug.ucaiug.org/UtiliComm/Shared%20Documents/Latest_Release_Deliv

erables/ 

with a file name syntax of “SG Network System Requirements Specification vN.R.xls”, 

where N represents the version number and R represents the revision number. This 

spreadsheet is referred to below as the Requirements Table. (as of this writing v5.1.xls) 

Instructions for how to document the business functional and volumetric requirements 

were prepared for the requirement authors, but also can be used by the consumer of the 

Requirements Table to better understand what is and is not included, and how to interpret 

the requirements data. The requirements documentation instructions are located at: 

 http://osgug.ucaiug.org/UtiliComm/Shared%20Documents/Latest_Release_Deliv

erables/ 

with a file name syntax of “rqmts-documentation-instructions-rN.R.doc”, where N 

represents the version number and R represents the revision number. 

The Requirements Table consists of several major sets of information for each use case. 

For example: 

• Business functional requirement statements are documented as individual 

information flows (e.g., specific application payload requirement sets). This is 

comparable to what many use case tools capture as information flows and/or 

illustrated in sequence diagram flows. 

• To the baseline business requirements are added: 

o the volumetric attributes (the when, how often, with what availability, 

latency, application payload size). Take note that the SG-Network TF 

Requirements Table definition for some terms (e.g., latency) is different 

than the classic network link latency usage. Please refer to the SGNetwork TF Requirements Documentation Instructions and the Smart Grid 

Networks System Requirements Specification Release Version 5 for the 

detailed definitions for clarification. 

o an assignment of the security confidentiality, integrity, and availability 

low-medium-high risk values for that application payload. 

• Payload requirement sets are grouped by rows in the table that contains all the 

detailed actor to actor passing of the same application payloads in a sequence that 

follows the main data flow from that payload’s originating actor to primary 

consuming actor(s) across possible multiple communication paths that a 
23 

deployment might use. The payload requirements’ sets will always contain a 

parent (main) actor to actor row and most will contain child (detailed) rows for 

that requirement set. 

• Payload communication path (information or data flow) alternatives that a given 

smart grid deployment might use. 

The process of requirements gathering and documentation has been evolutionary in 

nature as various combinations of additional attributes are documented; use cases added; 

payload requirement sets added; and alternative communication paths documented. The 

SG-Network TF has defined over 7,850 (as of release v5.1.xls; the basis of this work) 

functional and volumetric detailed requirements rows in the Requirements Table 

representing 204 different payloads for 19 use cases. 

SG-Network TF intends to continue this incremental version release approach to manage 

the scope and focus on documenting the requirements for specific use cases and payloads, 

yet giving consumers of this information something to work with and provide feedback 

for consideration in the next incremental releases. It is expected that the number of 

requirements rows in the Requirements Table will more than double, if not triple, from 

the current size when completed. 

To effectively use the business functional and volumetric requirements, the consumer of 

the Requirements Table must: 

• select which use cases and payloads are to be included 

• select which communication path scenario (alternative) is to be used for each of 

the main information / data flows from originating actor to target consuming actor 

• specify the size (quantity and type of devices) of the smart grid deployment 

• perform other tweaks to the payload volumetrics to match that smart grid 

deployment’s needs over time. 

The current Requirements Table (v5.1.xls) as a spreadsheet is not very conducive to 

performing these tasks. SG-Network TF is building a database that is synchronized with 

the latest release of the Requirements Table (spreadsheet). SG-Network TF will be 

adding capabilities to the database to: 

• solicit answers to the questions summarized above; 

• query the database; and 

• format and aggregate the query results for either reporting or exporting into other 

tools. 

The current SG-Network TF Requirements database and related user documentation are 

located at 

 http://osgug.ucaiug.org/UtiliComm/Shared%20Documents/Latest_Release_Deliv

erables/Rqmts_Database/ 

Note: SG-Network_Rqmts_Database_r5.1 is the version available for the database as of 

this writing. 
24 

3.5 Use of Smart Grid User Applications’ Quantitative Requirements for PAP02 Tasks 

Release 5.1 (March 5, 2012) of the SG-Network TF Requirements Table contains 

numerous use cases, payloads (applications), communication path options, and associated 

non-functional volumetric requirements data sufficient for a variety of smart grid 

deployment scenarios as input to PAP02. The instructions for how to adapt the SGNetwork TF’s Requirements for use in the SG framework and wireless modeling tool is 

discussed in section 6.5 of that document. 

As SG-Network TF continues to provide incremental Requirement Table releases and 

eventually completes that effort, that availability of quantified business functional and 

volumetric data will provide PAP02 and the reader of this report with a more complete 

set of smart grid business functional and volumetric requirement data for assessment of 

any given network standard and technology against. This is not a “do it once and it is 

completed” type of task. 

3.6 Security 

Security can be considered at every layer of the communication protocol stack, from the 

physical layer to the application layer. Security in the context of PAP02, which is mainly 

concerned with the physical and media access control layers, implies the inclusion of 

additional protocol and traffic events to achieve security signaling functionality as in the 

case of authentication and authorization, and additional bytes to existing payloads to 

achieve encryption. As a first step towards this goal, the SG-Network TF Requirements 

Table lists the security objectives of confidentiality, integrity, and availability (CIAs) for 

each event. As a second step, a mapping between these CIA levels (low / moderate / 

high) and the security protocols available at the various communication layers is needed 

in order to fully address security in the context of PAP02. 

 
25 

4 Wireless Technology 

PAP02’s task 5 calls for the collection of an inventory of wireless technologies. This 

inventory of wireless technologies is captured as a spreadsheet, “Wireless Functionality 

and Characteristic Matrix for the Identification of Smart Grid Domain Applications,” 

which can be found on the PAP02 web site: 

 http://collaborate.nist.gov/twiki-sggrid/bin/view/SmartGrid/PAP02Wireless

with a file name syntax of 

“Consolidated_Wireless_Characteristics_Matrix2_MM-DD-YY.xlsx”, where 

MM represents the month, DD represents the day, and YY represents the last two 

digits of the year. 

o https://collaborate.nist.gov/twikisggrid/pub/SmartGrid/PAP02Wireless/Consolidated_Wireless_Characteris

tics_Matrix2_09-03-13.xlsx 

OR 

 http://members.sgip.org/apps/org/workgroup/sgippap02wg/download.php/1610/2013-09-17_sgippap02wg_00015_Consolidated_Wireless_Characteristics_Matrix2_09-03-13.xlsx 

Disclaimer: The spreadsheet was created and populated by the Standards Setting 

Organizations, which proposed their wireless technologies as candidates for the smart 

grid. The parameters and metrics contained and values entered for each wireless 

technology were entered by the organizations representing those technologies. 

The next subsections give a brief description of the parameters and metrics contained in 

the spreadsheet, “Wireless Functionality and Characteristic Matrix for the Identification 

of Smart Grid Domain Applications” and a listing of the technologies submitted (as of the 

09-03-13.xlsx version). Note that this section is written with the assumption that the 

reader has a reasonable understanding of the wireless telecommunication terminology. 

4.1 Technology Descriptor Headings 

The spreadsheet identifies a set of characteristics and organizes these characteristics into 

logical groups. The group titles are listed below.

• Group 1: Applicable Smart Grid Communications Sub-Network(s) 

• Group 2: Data / Media Type Supported 

• Group 3: Range Capability (or Coverage Area When Applicable) 

• Group 4: Mobility 

• Group 5: Channel / Sector Data Rates and Average Spectral Efficiency 

• Group 6: Spectrum Utilization 

• Group 7: Data Frames, Packetization, and Broadcast Support 

• Group 8: Link Quality Optimization 

• Group 9: Radio Performance Measurement and Management 

• Group 10: Power Management 

• Group 11: Connection Topologies 
26 

• Group 12: Connection Management 

• Group 13: QoS and Traffic Prioritization 

• Group 14: Location Based Technologies 

• Group 15: Security and Security Management 

• Group 16: Unique Device Identification 

• Group 17: Technology Specification Source 

• Group 18: Wireless Functionality not Specified by Standards 

4.2 Technology Descriptor Details 

Each of these groups is composed of individual descriptive described in more detail 

below. 

4.2.1 Descriptions of Groups 1-7 Submissions 

Wireless Functionality and Characteristics Matrix for the Identification of Smart Grid 

Domain Application 

Functionality / Characteristic Measurement Unit 

Group 1: Applicable Smart Grid Communications SubNetwork(s) 

a: Primary SG sub-network(s) Select from 

HAN/FAN/NAN/WAN/etc. 

b: Secondary SG sub-network(s) Select from 

HAN/FAN/NAN/WAN/etc. 

Group 2: Data / Media Type Supported 

a: Voice Yes/No 

b: Data Yes/No 

c: Video 

Yes/No 

Group 3: Range Capability (or Coverage Area When 

Applicable) 

a: Theoretical range limitations at frequency km, GHz 

b: Conditions for theoretical range estimate PtP, PMP, LoS, non-LoS 

Group 4: Mobility 

a: Maximum relative movement rate km/h 

b: Maximum tolerated Doppler shift Hz 

Group 5: Channel / Sector Data Rates and Average 

Spectral Efficiency (Layer 2, or Note Other Layer if 

Applicable) 

a: Peak over-the-air uplink channel data rate Mb/s 

b: Peak over-the-air downlink channel data rate Mb/s 

c: Peak uplink channel data rate Mb/s 

d: Peak downlink channel data rate Mb/s 

e: Average uplink channel data rate Mb/s 

f: Average downlink channel data rate Mb/s 

g: Average uplink spectral efficiency (Mb/s)/Hz 

h: Average downlink spectral efficiency (Mb/s)/Hz 
27 

Wireless Functionality and Characteristics Matrix for the Identification of Smart Grid 

Domain Application 

Functionality / Characteristic Measurement Unit 

i: Average uplink cell spectral efficiency (Mb/s)/Hz 

j: Average downlink cell spectral efficiency (Mb/s)/Hz 

Group 6: Spectrum Utilization 

a:

Public radio standard operating in unlicensed bands

(DL and UL) GHz 

b:

Public radio standard operating in licensed bands 

(DL and UL) GHz 

c:

Private radio standard operating in licensed bands 

(DL and UL) GHz 

d: Duplex method TDD / FDD / H-FDD 

e: If TDD supported – provide details 

f: Channel bandwidth supported kHz 

g: Channel separation kHz 

h: Number of non-overlapping channels in band of 

operation Integer value 

i: Is universal frequency reuse supported? Yes/No 

Group 7: Data Frames, Packetization, and Broadcast 

Support 

a: Frame duration ms 

b: Maximum packet size bytes 

c: Segmentation support Yes/No 

d: Is unicast, multicast, broadcast supported? Yes/No 

4.2.1.1 Group 1: Applicable Smart Grid Communications Sub-Network(s) 

The Smart Grid communications network encompasses seven domains5 (as shown in 

Figure 1, Figure 2, and Figure 3 and listed in Table 1) with multiple actors and use cases 

that define communication paths for connecting actors within and between the seven 

domains. Multiple wireless solutions may be required to optimally meet the challenge of 

interconnecting actors and domains given a range of demographics, data requirements 

(e.g., capacity and latency), and propagation characteristics. The sub-networks group is 

intended to provide an assessment from the standards organization’s perspective as to 

where its specific wireless technology is best suited in the Smart Grid communications 

network. 

a) Primary SG sub-network(s): Based on the technology’s features and 

capabilities, for what SG sub-network is this technology best suited? Indoor 

Home Area Network (HAN), Field Area Network (FAN) or Neighborhood 

Area Network (NAN), Wide Area Network (WAN), Point-to-Point (PtP) 

backhaul, satellite, Any, etc. 

b) Secondary SG sub-network(s): Same choices as for Primary SG subnetwork(s) 

 

5

 NIST Special Publication 1108 , NIST Framework and Roadmap for Smart Grid Interoperability 

Standards, Release 1.0 
28 

For illustrative purposes Figure 4 shows an example of a Smart Grid communications 

network with sub-networks identified. Figure 5 provides additional detail to show the 

end-point (meter) connectivity in the AMI network. 

Figure 4 - Smart Grid communications sub-networks 
29 

Figure 5 - Expanded view of the AMI network 

4.2.1.2 Group 2: Data / Media Type Supported 

The information to be transferred within the smart grid includes data, voice, and video 

information. 

a) Voice: There is no specification of the codec being used but the assumption was 

that some form of packetized voice processing would be used and the connection 

would be two-way. Voice over Internet Protocol (VoIP) capacity should be 

derived assuming a 12.2 kb/s codec with a 50 % activity factor such that the 

percentage of users in outage is less than 2 % for a given bandwidth (please 

specify in simultaneous calls per MHz). If the VoIP’s conditions are different, 

please specify those assumptions. 

b) Data: is a generic term for information being transferred from machine to machine 

and can include information being displayed to a person for interpretation and 

further action. Please respond with yes/no. If yes, the details are provided in 

Group 5 and Group 13. 

c) Video: in cases where there is an outage and the situation in the field needs to be 

displayed to others remote from the outage site, video is desirable. Video could 

be still pictures or motion pictures. Please respond with yes/no. If yes, the details 

are provided in Group 5 and Group 13. 

4.2.1.3 Group 3: Range Capability (or Coverage Area When Applicable) 

Land-based wireless systems are designed to service a wide variety of application 

scenarios. The intent of this group is to capture the expected range in a typical 

deployment. Some systems are optimized for very short ranges, perhaps 10 m or less, 

while others are intended for longer ranges, perhaps on the order of tens of kilometers 

(e.g., 30 km). 
30 

The intent of this group is to capture the expected range in a theoretical deployment and 

gain a perspective regarding the most applicable Smart grid network segment to which 

the technology is best suited. 

A key deployment metric for satellite-based systems on the other hand, is the 

geographical size of the footprint covered. For these types of technologies the coverage 

area should be provided. 

When comparing range predictions for land-based systems, it is important to take into 

account both the Uplink (UL) and Downlink (DL) system gains and the margins assumed 

for fading, penetration loss, and interference, etc. These margins together with the 

system gain determine the UL and DL link budgets used to predict the range. It is also 

important to indicate the path loss model used and the type environment assumed; indoor, 

outdoor Line of Sight (LoS) or non-LoS urban, outdoor suburban, Point-to-Point (PtP) or 

Point-to-multipoint (PMP), etc., since these factors will also influence the range 

prediction. Note that the greatest range achievable by a specific technology typically 

requires transmission at the maximum Effective Isotropic Radiated Power (EIRP) 

permitted in the frequency band of operation and assumes the most robust modulation 

index. 

In some cases there may also be factors other than path loss and the link budget that place 

limits on the range. These factors may be latency-dependent features or other 

mechanisms built into the standard designed to optimize performance over a limited 

range of path lengths. If so, indicate if there is an inherent range over which the system is 

optimized, as well as a range for which the system is operational. 

4.2.1.4 Group 4: Mobility 

Some smart grid applications might require relative movement between a transmitter and 

receiver during the operation of the radio link. The inability of the radio link to operate 

successfully in situations of movement is due to many factors such as Doppler shift. This 

section covers Medium Access Control (MAC sublayer) and Physical layer (PHY). 

Higher layer mobility is covered in Group 12. 

This metric is intended to display the mobility capability of the radio technology in one 

or both of the two ways commonly used: 

a) Maximum relative movement rate (expressed in kilometers per hour) 

b) The maximum tolerated Doppler shift (expressed in Hertz) 

Mobile devices may not be able to communicate at the highest available data rates when 

moving at high speeds. 

4.2.1.5 Group 5: Channel / Sector Data Rates and Average Spectral Efficiency 

Channel data rates are a frequently used metric of radio link capability. The data rates for 

wireless technologies can span several orders of magnitude from a few bits per second up 

to several megabits per second, but so too can requirements for different smart grid 
31 

applications. Unless the conditions under which the data rates are determined are fully 

described and understood, channel data rate values can be misleading when used for 

comparative analysis. Additional complications stem from the fact that the data payload 

of interest is surrounded with additional bits used to provide error correction, error 

detection, address information, and a variety of control information. Because of these 

added bits the data payload or goodput will be considerably less than the total number of 

over-the-air (OTA) bits transmitted and received by a channel. In this context goodput, 

as defined in section 2.2, is the term used to describe the successful delivery of user data 

bits per unit of time at the application level, excluding protocol overhead and 

retransmitted data packets. 

Although goodput is the metric of most interest from a Smart Grid network application 

perspective, most wireless standards do not specify channel throughput or spectral 

efficiency at the application layer but instead focus on channel performance metrics at 

layer 1 and layer 2 (see Figure 6). For this group therefore, we ask for channel data 

throughput and spectral efficiency at the layer 2 - layer 3 interface. This is consistent 

with the evaluation methodology spelled out for International Mobile 

Telecommunications-Advanced (IMT-Advanced) in Report ITU-R M.21346

. In Figure 6 

this is noted as the MAC rate. The data throughput and spectral efficiency at this layer 

includes the overhead factors introduced at the PHY and the Data Link layer including 

the MAC sublayer. 

 

6

 Requirements related to technical performance for IMT-Advanced radio interfaces(s), see 

http://www.itu.int/pub/R-REP-M.2134-2008/en 
32 

Figure 6 – Layers in accordance with OSI model 

For the goodput it will be necessary to add the overhead introduced in the higher layers. 

These higher layer overhead factors would be quite similar for all technologies and 

include: 

• Payload size 

• Identity of the payload source 

• Identity of the payload destination 

• Security keys and encryption codes 

• Error correction and detection codes 

• Packet fragmentation codes 

• Acknowledgements 

There is also some overhead associated with establishing the data transmission channel 

(i.e., traffic channel) that is neither described above nor included in the goodput 

calculation. If this overhead value is available, it will be used in the framework and 

modeling tool. In addition there may be situations where packets are initially lost or 

corrupted and must be retransmitted. In these situations the data lost would further 

reduce the goodput delivery rate. 

It is also important to differentiate between downlink and uplink. Some radio systems 

are designed with uplink and downlink data rates that are equal in both directions, 

Layer 1: Physical Layer 

FEC/ARQ/HARQ

Layer 2: Data Link Layer

MAC Sublayer, 

Frame Header, QoS, etc.

Layer 3: Network or IP

Smart Grid Actor A

Application Layer

Layer 4: Transport

Layer 2: Data Link Layer

MAC Sublayer

Frame Header, QoS, etc.

Layer 3: Network or IP

Smart Grid Actor B

Application Layer

Layer 4: Transport

Layer 1: Physical Layer

FEC/ARQ/HARQ

Goodput

1-Way 

Latency

Over the Air Rate

PHY Rate

MAC Rate

Network / IP 

Rate

Layer 5: Session Layer 5: Session

Layer 6: Presentation Layer 6: Presentation

TCP/IP Model 

Application Layer

TCP/IP Model 

Transport 
33 

whereas others support asymmetric rates. DL (also known as forward link or out-route) 

represents the data transmission from the central transmitter or base station (BS) to the 

client device receiver. UL (also known as return link or in-route) represents the data 

transmission from the client device transmitter to the central receiver. Typically the 

asymmetry is designed to provide a higher downlink rate than the uplink rate. This 

allows a central station or BS to take advantage of higher antenna height and transmit 

power that may not be practical on the client device. 

There are several goals for the information submitted for this group. One is to get a 

measure of the peak OTA channel data rate in the UL and DL direction. A second goal is 

to get an assessment of the peak UL and DL channel data rates at layer 2. The latter 

value accounts for all of PHY and Data Link layer overhead including: error correction, 

control bits, packet headers, etc. The third goal is to gain a perspective for the average 

channel throughput and average channel spectral efficiency at the layer 1 - layer 2 

interface for both the UL and DL channels. 

Spectral efficiency is an important metric that measures how much data a given system 

can carry per unit of spectrum, and is typically given in units of bits/s/Hz. It is highly 

dependent on the channel modulation and coding scheme (MCS) being used. The 

average channel data capacity or average channel spectral efficiency is directly related to 

the average MCS over the channel or sector coverage area. Most, if not all, of today’s 

access technologies make use of adaptive modulation and coding to account for 

differences in propagation path conditions on a link by link basis to individual user 

terminals. Terminals or client devices at or near the cell edge would be linked with the 

most robust MCS, which has relatively low spectral efficiency, whereas terminals close 

to the BS would generally experience a higher Signal to Noise Ratio (SNR) and thus 

support a higher efficiency MCS. The average MCS used by a terminal would lie 

somewhere between these two extremes. For comparative purposes, having an estimate 

for the average MCS and ultimately the average channel data rate is very desirable but, 

unfortunately, arriving at these values is not a straightforward process as it depends on a 

large number deployment-related factors. Most wireless access technologies have a 

specific evaluation methodology to simulate channel performance for typical deployment 

scenarios for either indoor or various outdoor venues. Although these evaluation 

methodologies have a lot of similarities they often cover a wide range of deployment 

scenarios and require a number of parameter inputs and assumptions to perform the 

simulations. Since reported results will often be based on different sets of assumptions, 

these simulations tend to be technology-specific. It is necessary therefore, to exercise care 

when using information derived from these simulations for comparative purposes. 

To gain a better understanding for assessing the channel data rate and spectral efficiency 

at the layer 2 - layer 3 interface resulting from these simulations, this group provides the 

characteristics of the applicable evaluation methodology together with details regarding 

the input parameters used for the simulations. 

The relationship between the net cell spectral efficiency and channel / sector spectral 

efficiency is dependent on the frequency reuse factor. For frequency reuse of 1 they will 
34 

be the same whereas for a reuse factor of n the cell spectral efficiency will be 1/n times 

the sector spectral efficiency. 

It is anticipated that the data rate and spectral efficiencies reported will typically apply to 

the layer 2 - layer 3 interface as described above. If for any wireless technology, these 

values are known for higher layers it should be noted. 

Addendum to Group 5: Provide the characteristics of the evaluation methodology and the 

parameter assumptions for the simulations used to arrive at the average channel data rate and 

average spectral efficiency values in Group 5 

Note: If these parameters are not applicable to your specific technology, please provide a set of 

assumptions corresponding to your technology that were used in your simulation 

1) Base station cluster size Integer value (e.g., 19) 

2) Sectors per base station Integer value (e.g., 3) 

3) Frequency GHz 

4) Channel bandwidth MHz 

5) BS to BS spacing km 

6) BS antenna pattern Omni or Azimuth in degrees and Front-toBack Ratio in dB 

7) Base station antenna height m 

8) Mobile terminal height m 

9) BS antenna gain dBi 

10) MS antenna gain dBi 

11) BS maximum Tx power dBm 

12) Mobile terminal maximum Tx power dBm 

13) Number of BS (Tx)×(Rx) antennas Integer value (e.g., 2×2) 

14) Number of MS (Tx)×(Rx) antenna Integer value (e.g., 1×2, 2×2, etc.) 

15) BS noise figure dB 

16) MS noise figure dB 

17) Frequency reuse factor Integer value 

18) Duplex FDD / H-FDD / TDD 

19) If TDD, what is UL to DL channel 

bandwidth ratio? 

Ratio (e.g., 2 to 1) 

20) Active users per sector or per BS Integer value (e.g., 10 users per sector) 

21) Path loss model (specify model or 

provide values for A in dB and n) 

PL = AdB + 10nlog10(d); where d is in km or 

COST231, WINNER II, etc. 

22) Environment or terrain type Indoor or Outdoor-urban / Outdoor-suburban, 

Urban-Micro-cell, etc. 
35 

23) Log-normal shadowing standard 

deviation 

dB 

24) Penetration loss (if applicable) dB 

25) Other link margins (if applicable) i.e., 

fast fading, interference, etc. 

dB 

26) Traffic type FTP, VoIP, mixed, etc. 

27) Multipath channel model and 

distribution 

% Ped A, % Ped B, % Veh A, % Stationary, 

etc. 

28) Number of paths Integer value 

4.2.1.6 Group 6: Spectrum Utilization 

This group asks for display of information on radio spectrum use. 

a) Public radio standard operating in unlicensed band 

b) Public radio standard operating in licensed band 

c) Private radio standard operating in licensed band 

Some radio spectrum is license-exempt and is shared among a wide variety of devices. 

An example of this would be the 2.4 GHz Industrial Scientific and Medical (ISM) band 

which is generally available anywhere in the world but shared among diverse radio 

technologies, such as cordless phones, IEEE Std. 802.11 wireless local area networks 

(WLANs), IEEE Std. 802.15 personal area networks (including Bluetooth) devices, to 

name a few. 

Some spectrum is sold and licensed to individual entities, such as a mobile phone service 

provider, and the designated spectrum (at least on a regional basis) is not expected to be 

used by any other radio type. 

d) Duplex method - It is also generally assumed that smart grid radios will be both 

transmitting and receiving information. One method used to accomplish bidirectional transfer is time division duplexing (TDD) where uplink and downlink 

packets are alternated in time. Another method is frequency division duplexing 

(FDD) where uplink and downlink packets are carried on different frequencies. 

With FDD, DL and UL transmissions can take place simultaneously. A third 

duplexing approach is Half-duplex FDD (H-FDD). H-FDD also uses two 

separate channels but does not support simultaneous DL and UL transmissions. 

Some access technologies support both FDD for terminals which have a 

duplexing filter and H-FDD to support terminal designs which do not have a 

duplexing filter. 

When TDD is supported, technologies may also support adaptive or adjustable 

DL to UL traffic flow to improve channel spectral efficiency when traffic patterns 

are highly asymmetrical. For multi-cellular deployments adaptive TDD requires 
36 

some form of sector-to-sector and cell-to-cell synchronization to mitigate 

interference. 

e) If TDD is supported, provide details and characteristics. For example, Is adaptive 

or adjustable TDD supported and what synchronizations methods are employed? 

f) Channel bandwidth - As with data rates, some radios use a very small amount of 

radio spectrum for their channel bandwidths (perhaps a few kilohertz (kHz)) 

while others may use a very large swath (perhaps several megahertz (MHz)). 

g) Channel separation - This metric is intended to report the separation between 

channels. 

h) Non-overlapping channels in the band 

To use an example, some IEEE Std. 802.11 radios operate in the 2.4 GHz 

unlicensed ISM band. Within the US there is 83.5 MHz of spectrum 

available; however, there are restrictions on out of band emissions 

(Described in Federal Communications Commission (FCC) Title 47). 

IEEE Std. 802.11 initially chose to use a spread spectrum technology that 

occupied 20 MHz of channel bandwidth. When the FCC rules and the 

technology choices are combined, the result is a technology that has 11 

operating channels defined with center carrier frequencies separated by 

5 MHz. Hence, in the 2.4 GHz band, the IEEE Std. 802.11 technology 

would be described as having 11 operating channels, separated by 5 MHz 

and three non-overlapping channels. 

i) Support for universal frequency reuse - Most outdoor terrestrial deployments will 

use multi-sector BSs, with 3-sector BSs being the most common and the 

configuration most often assumed for simulations. Universal frequency reuse or a 

reuse factor of 1 indicates that the same channel can be reused in each of the three 

sectors. A reuse factor of three indicates that each sector is deployed with a 

unique channel. This deployment configuration requires three times as much 

spectrum as reuse 1 but will generally result in greater immunity to sector-tosector and cell-to-cell interference. Although the channel or sector spectral 

efficiency will be higher for reuse three the increase is generally not sufficient to 

offset the fact that three times as much spectrum is required. The net cell spectral 

efficiency, therefore, will generally be higher with universal frequency reuse. 

4.2.1.7 Group 7: Data Frames, Packetization, and Broadcast Support 

This group asks for display of information on the packetization process. 

A frame is defined as one unit of binary data that can be sent from one device to another 

device (or set of devices) sharing the same link. The term is used to refer to data 

transmitted at the Open Systems Interconnection (OSI) model’s Physical or Data Link 

layers (layer 1 and layer 2). 

A packet is defined as one unit of binary data that can be routed through a computer 

network. The term is used to refer to data transmitted at the OSI model’s network layer 

(layer 3) and above. 

a) What is the frame duration? 
37 

b) What is the maximum packet size that can be sent in one radio frame? 

c) Does the radio system support layer 2 segmentation when the payload size 

exceeds the capacity of one radio frame? 

d) Are unicast, multicast, and broadcast supported? (yes/no for each) 

• Unicast: unicast is a form of message transmission where a message is sent 

from a single source to a single receiving node. 

• Multicast: multicast is a form of message transmission where a message is 

sent from a single source to a subset of all potential receiving nodes. (The 

mechanism for selecting the members of the subset is not part of this 

definition.) 

• Broadcast: broadcast is a form of message transmission where a message is 

sent from a single source to all potential receiving nodes. 

4.2.2 Descriptions of Groups 8-12 Submissions 

Wireless Functionality and Characteristics Matrix for the Identification of Smart Grid 

Domain Application 

Functionality / Characteristic Measurement Unit 

Group 8: Link Quality Optimization 

a: Diversity technique antenna, polarization, 

space, time 

b: Beam steering Yes/No 

c: Retransmission ARQ / HARQ / - 

d: Forward error correction technique Yes/No (if Yes, please 

provide details) 

e: Interference management Yes/No (if Yes, please 

provide details) 

Group 9: Radio Performance Measurement and 

Management 

a: RF frequency of operation GHz 

b: Configurable retries? Yes/No (if Yes, please 

provide details) 

c: Provision for received signal strength indication 

(RSSI) 

Yes/No (if Yes, please 

provide details) 

d: Provision for packet error rate reporting Yes/No (if Yes, please 

provide details) 

Group 10: Power Management 

a: Mechanisms to reduce power consumption Yes/No (if Yes, please 

provide details) 

b: Low power state support Yes/No (if Yes, please 

provide details) 

Group 11: Connection Topologies 
38 

Wireless Functionality and Characteristics Matrix for the Identification of Smart Grid 

Domain Application 

Functionality / Characteristic Measurement Unit 

a: Point-to-point (single-hop) Yes/No (if Yes, please 

provide details) 

b: Point-to-multipoint (star) Yes/No (if Yes, please 

provide details) 

c: Multi-hop or multi-link Yes/No (if Yes, please 

provide details) 

d: Statically configured or self-configuring multi-hop Yes/No (if Yes, please 

provide details) 

e: Dynamic and self-configuring multi-hop network Yes/No (if Yes, please 

provide details) 

Group 12: Connection Management 

a: Handover Yes/No (if Yes, please 

provide details) 

b: Media access method (if applicable) Specify (e.g., 

CSMA/CD, Token, 

etc.) 

c: Multiple access methods Specify (e.g., CDMA, 

OFDMA, etc.) 

d: Discovery Yes/No (if Yes, please 

provide details) 

e: Association Yes/No (if Yes, please 

provide details) 

4.2.2.1 Group 8: Link Quality Optimization 

Radio systems can use a variety of techniques to improve the likelihood a transmitted 

packet will be successfully received. The most fundamental technique is to have the 

receiving radio send an acknowledgement back to the transmitting station. If the 

acknowledgement is not received, then the transmitter will try again (up to some limit of 

retries). This is called link layer Automatic Repeat-reQuest (ARQ). Other techniques 

seek to improve the SNR at the receiver. These techniques include diversity, advanced 

antenna systems such as beam steering, and forward error correction. 

Interference can also impact link performance. Co-Channel Interference (CCI) can be 

caused by interference (Intra-operator interference) from adjacent sectors or other BSs in 

close proximity to the transmission link of interest. Adjacent channel interference may 

arise from systems operating in adjacent frequency bands (inter-operator interference). 

With shared spectrum, as would be the case in unlicensed bands, CCI can also arise from 

other wireless networks operating in the same geographical region. Some wireless 

systems have the capability of detecting and either avoiding or at least mitigating the 

impact of interfering signals to enhance Signal to Interference plus Noise Ratio (SINR). 
39 

4.2.2.2 Group 9: Radio Performance Measurement and Management 

This group is used to indicate what the radio technology provides to an administrator to 

assist in link assessment. Most radio systems dynamically and autonomously assess their 

environment and adjust to optimize performance. Sometimes it is useful for a network 

administrator to monitor behavior to determine if problems exist that are impeding 

performance or perhaps make manual selections that might indeed improve radio 

performance beyond what might be achieved autonomously. 

4.2.2.3 Group 10: Power Management 

Radio devices may not be directly powered by mains power supply and may be required 

to “run off” a battery that is seldom, if ever, recharged. The intent is to capture 

information on techniques that the radio technology has defined that can be used to 

reduce power consumption. 

4.2.2.4 Group 11: Connection Topologies 

Radio systems may be designed and configured to use one or more connection 

topologies. A common topology is the star or point-to-multipoint topology as illustrated 

in Figure 7. This topology is common in today’s mobile (cellular) and fixed local area 

and wide area networks and can be expected to be a widely used topology in Smart Grid 

networks. 

Figure 7 - Star or point-to-multipoint topology 

Other wireless topologies that may be present in Smart Grid communication networks 

include the following: 

a) Single-hop network: Also known as point-to-point, a single-hop network is one 

in which devices can only communicate with each other directly, e.g., over a 

single link (hop), and do not have the capability to forward traffic on each other’s 

behalf. 

b) Multi-hop network: A multi-hop network is one in which devices have the 

capability to forward traffic on each other’s behalf and can thus communicate 

along paths composed of multiple links (hops). 
40 

A multi-hop network can take two forms. One form is a daisy chain of 

links (hops) that consists of a number of serial or tandem connected 

devices. This could serve to extend the reach of the network beyond the 

reach of an individual link. An example of this is illustrated on the left 

side of Figure 8. The other form of a multi-hop network is to form a tree 

or mesh topology. This could serve to provide connectivity to a number of 

devices located in a common geographic area, for example a number of 

AMI meters located in a neighborhood. An example of this is illustrated 

on the right of Figure 8. It should be noted in the example network 

diagram (Figure 8) that the two forms could be combined to extend the 

reach of the backhaul link to a mesh network. 

i. Statically configured multi-hop network: A multi-hop network can 

be statically configured, such that each node’s forwarding 

decisions are dictated by its pre-configured forwarding table. 

ii. Dynamic and self-configuring multi-hop network: A multi-hop 

network can be dynamic and self-configuring, such that network 

devices have the ability to discover (multi-hop) forwarding paths 

in the network and make their own forwarding decisions based on 

various pre-configured constraints and requirements, e.g., lowest 

delay or highest throughput. This is a typical characteristic of 

current AMI mesh networks. 

Figure 8 - Network diagram showing two types of multi-hop networks 

4.2.2.5 Group 12: Connection Management 

This group is intended to capture the capabilities provided to initiate and maintain radio 

connectivity. 

a. Handover 

b. Media access method, if applicable (e.g., CSMA/CD, Token, etc.) 

c. Multiple access method (e.g., CDMA, OFDMA, etc.) 

d. Discovery: The ability for the stations to discover available APs / routers / BSs in 

the area. 

e. Association: Once authentication has completed, stations can associate (register) 

with an Access Point (AP) / router / BS to gain full access to the network. The 
41 

association is binding between the terminal or client and an AP such that all 

packets from and to the client are forwarded through that AP. Association 

typically involves the exchange of a small number of packets. 

4.2.3 Descriptions of Groups 13-17 Submissions 

Wireless Functionality and Characteristics Matrix for the Identification of Smart Grid 

Domain Application 

Functionality / Characteristic Measurement Unit 

Group 13: QoS and Traffic Prioritization 

a: Radio queue priority Yes/No (if Yes, 

please provide 

details) 

b: Pass-thru data tagging Yes/No (if Yes, 

please provide 

details) 

c: Traffic priority Yes/No (if Yes, 

please provide 

details) 

Group 14: Location Based Technologies 

a: Location awareness (x,y,z coordinates) Yes/No (if Yes, 

please provide 

details) 

b: Ranging (distance reporting) Yes/No (if Yes, 

please provide 

details) 

Group 15: Security and Security Management 

a: Encryption Algorithms 

supported, AES Key 

length, etc. 

b: Authentication Yes/No (if Yes, 

please provide 

details) 

c: Replay protection Yes/No (if Yes, 

please provide 

details) 

d: Key exchange Protocols supported 

e: Rogue node detection Yes/No (if Yes, 

please provide 

details) 

Group 16: Unique Device Identification 

a: MAC address Yes/No (if Yes, 

please provide 

details) 
42 

Wireless Functionality and Characteristics Matrix for the Identification of Smart Grid 

Domain Application 

Functionality / Characteristic Measurement Unit 

b: Subscriber identity module (SIM) card Yes/No (if Yes, 

please provide 

details) 

c: Other identity Specify 

Group 17: Technology Specification Source 

a: Base standard SDO SDO name 

b: Profiling and application organizations Association / Forum 

Name 

4.2.3.1 Group 13: QoS and Traffic Prioritization 

Quality of Service (QoS) is a term that is used to describe a technology’s ability to 

provide differentiated levels of performance to selected types of traffic. QoS can be 

viewed as an end-to-end requirement, but some radio systems assist in the process by 

supporting QoS between radio nodes. Generally this involves the ability to tag different 

data packets to establish a range of packet-priorities consistent with the type of 

information carried by the packet. QoS can be used to set priorities on data packets to 

ensure that there is sufficient bandwidth and that jitter, latency, and packet error rates are 

consistent with that required for satisfactory performance for the traffic type carried by 

the packet, whether it is voice, data, or streaming video. 

Traffic categories fall into two generic types: 

• real time - describing services that are sensitive to latency, jitter, and require a 

Guaranteed Bit Rate (GBR) for satisfactory performance; and 

• non-real time - for services that are much more tolerant to variations in latency, 

jitter, and data rate. 

Additionally, a Maximum Bit Rate (MBR) may also be imposed with any traffic type to 

prevent over-subscription by a single user or application. 

Examples of real time or GBR services include: 

• T1 / E1 leased line 

• Voice with or without silence suppression 

• Videoconferencing 

• Real time gaming 

• Streaming video or audio 

Examples of non-real time or non-GBR services7

 include: 

• IP Multimedia Subsystem (IMS) signaling and unicast polling 

• Buffered video or audio 

• Other services such as: web browsing, E-mail, file transfers (FTP), etc., 

This group is used to capture information regarding the capabilities for managing traffic 

priorities and supporting QoS. An important metric is the number of priority levels that 

are supported for either real time (or GBR) or non-real time (non-GBR) traffic. 

 

7

 Best effort is a term often used to describe services in this category. 
43 

a. Radio queue priority refers to the ability of radio nodes to prioritize packets that 

are queued for transmission. 

b. Pass-thru data tagging refers to the ability to transfer successfully packets that use 

a class of service priority tag, such as those defined by IEEE Std. 802.1p / 802.1Q 

c. Traffic priority refers to the ability of radio systems to use high level priority. 

4.2.3.2 Group 14: Location Based Technologies 

Radio systems that provide information about their location can be helpful. One common 

form of location information would provide three-dimensional information regarding 

position, such as that provided via Global Positioning System (GPS) coordinates. Some 

technologies rebroadcast GPS ephemeris and almanac in an assisted GPS channel in 

order to reduce acquisition time for the GPS receiver. An alternate form would provide 

range information such that when the absolute location of every node is not known; if the 

location of one radio device was known, then at least the distance between the nodes 

could be provided. 

4.2.3.3 Group 15: Security and Security Management 

Ensuring that smart grid data is transferred securely is a high priority8

. As with other 

entries such as QoS there are options to apply security measures at multiple layers in the 

communications OSI model. This group focuses on options provided by the radio system 

at layer 1 (PHY) and layer 2 (MAC). 

4.2.3.4 Group 16: Unique Device Identification 

It is desired that each radio node be directly identifiable and addressable. This requires 

that each device have a unique identification scheme. There is more than one way to 

accomplish this. The information provided will identify the unique identification scheme 

offered. 

4.2.3.5 Group 17: Technology Specification Source 

The intent is to provide information about the SDO that developed and maintains the 

radio technology, plus identify who provided the information contained in the matrix. 

Also, in some cases the base standard source is assisted by a compatriot organization that 

provides additional support including specifications or applications that operate above 

layer 2. The supporting organizations may also provide certification of specification 

compliance, interoperability and performance. 

4.2.4 Group 18: Wireless Functionality not Specified by Standards 

We asked the SDOs to provide ranges of values for these parameters which are generally 

not directly specified in the standard and will often be vendor-specific. Since these 

parameters play a key role in determining wireless performance, it is incumbent on the 

utility companies to work with their vendors to get more accurate values for these 

parameters. 

The ranges provided are typical (not exhaustive) based on the experiences of the SDO 

community that has provided them. 

 

8

 NISTIR 7628 Volumes 1 and 2 
44 

Wireless Functionality and Characteristics Matrix for the Identification of Smart Grid 

Domain Application 

Functionality / Characteristic Measurement Unit 

Typical wireless functionality NOT directly specified by a 

standard that is needed in quantifying operating metrics 

 Rx sensitivity dBm 

 Base station Tx peak power dBm 

 Subscriber station / user terminal Tx peak power dBm 

 Base station antenna gain dBi 

 Subscriber station / user terminal antenna gain dBi 

 Receiver thermal noise floor dBm/Hz 

Following is a list of additional characteristics that are needed to fully characterize the 

performance of the radio in a typical operating environment. 

• Rx sensitivity - Receiver sensitivity may be specified as a minimum capability 

required by the SDO in the technology specification. Technology 

implementations may provide much greater sensitivity than the minimum, so the 

intent is to capture a typical value that is used for the operating point calculations. 

• Base station Tx peak power – Transmission peak power to the antenna is needed 

for range calculations as well. Some technologies specify only a regulatory limit 

or allow for a number of options. The Tx power of the devices under 

consideration for the operating point calculations needs to be specified. 

• Subscriber station (SS) / user terminal Tx peak power – Typical transmission peak 

powers delivered to the antenna for different user terminals are needed for range 

calculations as well. 

• Base station antenna gain – BS antenna gain is rarely part of a technical radio 

standard, but is a critical component of link budget calculations. 

• SS / user terminal antenna gain – Terminal antenna gains are rarely part of a radio 

standard and will also vary with the type of terminal. Where applicable provide 

typical antenna gains for different types of terminals. 

• Thermal noise floor – Thermal noise floor is much like receiver sensitivity. There 

might be a minimal specification for noise floor required by the SDO in the 

technology specification. Technology implementations may provide a much 

lower noise floor than the minimum, so the intent is to capture a typical value that 

is used for the operating point calculations. 

Although not specifically requested for in the capabilities matrix, the modulation and 

coding scheme is relevant for fully assessing the performance of the wireless 

technologies. We encourage the utility companies to work with their vendors to get the 

information regarding the modulation and coding schemes used by the corresponding 

technology. 

Modulation is a method used to encode digital bits into a radio signal. There are dozens 

of different types of modulation technologies employed in wireless technologies. 

Modulation technologies are typically associated with an acronym. Acronyms that are 
45 

commonly encountered include BPSK (binary phase shift key), FSK (frequency shift 

key), QAM (quadrature amplitude modulation) and dozens of variations on these themes. 

Simple modulation schemes convey one bit per time unit while high order modulation 

schemes can convey multiple bits per time unit. Transmission physics require that a 

relatively high signal to noise ratio exist at the receiver to enable low error decoding. 

Since entire books are dedicated to the topic, it is not appropriate for this guideline to try 

and identify or describe modulation options in detail. 

Similarly, there are a wide variety of coding schemes for forward error correction (FEC), 

which are used to detect and correct errors incurred during transmission and reception. 

FEC adds bits to the transmitted data stream that are used by the receiver, in a carefully 

engineered algorithm, to determine if there were any errors in the reception and correct 

those errors if possible. There are numerous ways to construct the code and algorithms 

and a technical description of all the options is outside the scope of this guideline. 

A transmission is comprised of a combination of modulation and coding. Each 

combination of a modulation and coding is referred to as a modulation and coding 

scheme (MCS). One wireless technology may have only a few such combinatorial 

options while another may have hundreds. 

The reason for having options is to provide the wireless technology with a means to 

dynamically adapt the transmission in order optimize goodput under changing radio 

environments. This wireless dynamic is referred to as link adaptation or adaptive 

modulation and coding. 

For example, high order modulation schemes such as 256 QAM require a significant 

signal to noise ratio in order to deliver packets at an acceptable packet error rate. If the 

signal strength falls, then the wireless system needs to choose a different combination of 

modulation and error correction to reduce packet errors and maintain the radio link. 

4.3 Wireless Technology / Standard Submissions 

Responses have been received for the following families of wireless access technologies / 

standards: 

• ITU-T G.9959 (Z-Wave®) 

• IG Band 

• IEEE Std. 802.11TM family 

• IEEE Std. 802.15.4TM 

• IEEE Std. 802.16TM family (WiMAX® / WiGRIDTM) 

• GSM® Enhanced Data rates for GSM Evolution (EDGE) 

• CDMA2000® 1x, High Rate Packet Data (HRPD) / EVDO and Extended Cell 

High Rate Packet Data (xHRPD) 

• UTRAN (W-CDMA) and Evolved High-Speed Packet Access (HSPA+) 

• E-UTRAN (Long Term Evolution (LTETM)) 

• Fixed Satellite Services (FSS) and Mobile Satellite Services (MSS) 
46 

Table 3 contains a more detailed listing of the submitted wireless technologies along with 

the sub-network for which it was designated for smart grid usage and the type of 

spectrum specified (i.e., licensed or unlicensed or both). Table 3 also indicates which 

technologies are assessed in section 6.7 for meeting SG network requirements. The 

framework and modeling tool used for this assessment is limited to terrestrially-based 

outdoor-located BSs with a PMP topology operating in frequency bands from 700 MHz 

to 6000 MHz. 

Table 3: Listing of wireless technologies submitted

Wireless Technology Sub-network 

(submitted) 

Assessed 

in 

section 

6.7 

Licensed 

(L) or 

Unlicensed 

(UL) 

Spectrum 

ITU-T G.9959 and Z-Wave wireless 

technologies 

HAN UL

IG Band (450 MHz - 470 MHz) NAN, WAN L

IEEE Std. 802.11 HAN, FAN ● UL

IEEE Std. 802.11ah – Indoor / Outdoor HAN, FAN, 

NAN ● UL 

IEEE Std. 802.11n HAN, FAN ● UL

IEEE Std. 802.11ac HAN, FAN ● UL

IEEE Std. 802.15.4 HAN, FAN, 

NAN ● L, UL 

IEEE Std. 802.16-2012 / WiMAX WAN, FAN, 

NAN 

● L, UL

IEEE Std. 802.16.1-2012 / WiMAX 2 WAN, FAN, 

NAN 

● L, UL

IEEE Std. 802.161a-b / WiGRID WAN, FAN, 

NAN 

● L, UL

GSM / EDGE Radio Access Network (GERAN) WAN ● L

cdma2000 1x WAN ● L

cdma2000 High Rate Packet Data (HRPD / EVDO) WAN ● L

Extended High Rate Packet Data (xHRPD) WAN ● L

Universal Terrestrial Radio Access Network 

(UTRAN) (a.k.a. Wideband CDMA (WCDMA)) 

WAN ● L

Evolved High-Speed Packet Access (HSPA+) WAN ● L

Evolved Universal Terrestrial Radio Access 

Network (E-UTRAN) (a.k.a. Long Term 

Evolution (LTE)) 

WAN ● L

Mobile Satellite Service (MSS) in L / S-Band WAN L

Fixed / Mobile Satellite Service (FSS / MSS) in 

Ku/Ka-band 

WAN L
47 

5 Modeling and Evaluation Approach 

Determining an assessment method for evaluating whether a wireless technology can 

satisfy the smart grid user applications’ requirements is a daunting task, especially given 

that there are many possible physical deployment options for smart grid devices and 

facilities, many wireless technology standards, and uncertainty in anticipating future 

needs. 

Some wireless technologies are a part of a larger system, while others are complete 

communication networks. For example, wireless technologies developed by many IEEE 

802 working groups consider mostly the MAC sublayer and PHY. In many such cases, 

other non-IEEE specifications are used as the basis of a complete network specification. 

For example, the WiMAX Forum provides complete end-to-end specifications for fixed 

and mobile networks based on the IEEE Std. 802.16. Likewise, the Universal Mobile 

Telecommunications System (UMTS) is a complete mobile (and wireless) network 

system. For many reasons, including the differing scope of the basic specifications, 

comparing wireless technologies is a daunting task. PAP02 assesses different wireless 

technologies and provides tools and guidelines to help determine to what extent they can 

satisfy smart grid use case requirements but PAP02 will not attempt to rank the various 

wireless technologies relative to each other. 

5.1 Assessment of Wireless Technologies with Respect to Smart Grid Requirements 

The following assessment approach should be considered as an example, not the 

approach that must be used. Options are discussed on how the assessment can be refined 

by techniques further described and detailed in this section’s subsections. 

The two main tasks are: 

1) Perform an initial screening of the wireless technologies against the smart grid 

business functional and volumetric requirements and

2) Perform refinements to the initial screening using one or a combination of the 

following: 

• Mathematical models 

• Simulation models 

• Testbeds (lab and in the field) 

5.1.1 Initial Screening 

The initial screening (technology assessment) is based on the smart grid user 

applications’ requirements in section 3.4 and the wireless functionality and characteristics 

matrix in section 4. For example, a smart grid’s application’s requirement for reliability 

should be related to the wireless technology’s availability to establish and maintain a 

communication link with an acceptable error rate. Likewise, smart grid requirements for 

range, data capacity, and latency must be considered when selecting technologies for 

further evaluation. One can use the results from the initial assessment provided in section 

4 to determine whether a given wireless technology should be further considered for use 

in a particular network segment in a large scale smart grid communications network 

deployment. In making the wireless assessments it is very important to carefully consider 
48 

the differences in baseline assumptions used for the different wireless technologies to 

arrive at the values entered into the matrix. 

5.1.2 Refinements to Initial Screening 

After the initial screening, the next step is to refine the assessment using other methods 

(i.e., mathematical models, simulations models, or testbeds). 

5.1.2.1 Mathematical Models 

These types of models require creating mathematical model representations that 

approximate the characteristics of the system in question (e.g., the smart grid). 

Mathematical models are often based on a combination of analytical and empirical 

techniques. These models can be simplistic in that event data volumetrics are aggregated 

to singular values, or events are treated as individual inputs into the models, or data 

volumetrics represented as inputs based on probabilities. Mathematical models usually 

take less time to produce results than simulation models, but there are some limitations to 

what some of the simpler mathematical models can adequately model. 

5.1.2.2 Simulation Models 

Simulation models attempt to account for more of the event occurrence variability than 

was described in the mathematical model discussion above. Since they take into account 

a greater number of variables, simulation models can provide more realistic results than 

mathematical models, which often require simplifying assumptions to make them 

tractable. As was shown in section 4, group 5, simulation models take into account a 

large number of deployment and equipment parameters resulting in results that are 

technology-specific making it difficult to make accurate comparisons. Although it would 

be desirable to have commonly accepted simulation model applicable to all of the 

wireless technologies, the development of such a model would be a complex and timeconsuming process that is beyond the scope of this report. 

5.1.2.3 Testbeds 

Usually, neither mathematical or simulation model types are able to capture all of the 

details of a proposed network deployment (e.g., accurate channel models are difficult to 

obtain without direct measurement of the deployment environment). Using testbeds (in 

the lab and, preferably, in the field) can provide very accurate results; however, this 

method requires significant time, effort, and resources to produce results. Testbed results 

may also be provided as feedback to mathematical and simulation models to further 

validate or enhance the results. 

5.1.2.4 Network Design 

The key for network design is to understand and define the network’s system design 

goals. Designing a network system to support the average data requirements is one 

design concept, which tends to result in under designed and built networks. Another 

concept is to design network systems that can handle the absolute worst case imaginable, 

which tends to result in over designed and built networks. Again the key is to establish a 

goal of the network and of the individual elements and threads of that network so that it 

will handle the heaviest expected (combined) burst rates with an acceptable level of 
49 

failure. For example, in the old telephone trunk design days, one would specify the 

number of voice trunks necessary to carry the busy hour traffic with an acceptable level 

of failure (2 % failure, 5 % failure, etc.). This then leads to two questions that the 

network designers and implementers need to address, but are not answered in this 

guideline: 

1) What is this highest level of traffic that must be accommodated over a specified 

burst period(s)? 

a. The methods for determining this will be highly dependent on the 

individual utility operational modes and the aggregated data that will flow 

through a particular network link or thread. As you can imagine, this will 

vary greatly from utility to utility and with the topology / technology used 

to construct the network threads. 

2) What is an acceptable level of overloading these threads that will result in failure 

to deliver the data within the required latency and integrity constraints? 

a. This will depend on multiple factors, including the latency and integrity 

requirements of the system or application, buffering capabilities to buffer 

overflow traffic, and how error recovery is accomplished. 

The utilities will need to implement systems that will satisfy the needs of that specific 

utility (i.e., one size does not fit all). So the network designers will need to find a way to 

project and predict the real temporal (and spatial) requirements of the data flows (for the 

utility, application, or operating mode in question) and then select and implement 

technologies and topologies that will provide the needed capacity, reliability, security, 

cost effectiveness, etc. 

A general modeling framework was developed by the PAP02 working group and it is 

described in section 5.2. 

5.2 Modeling Framework 

The goal of the development process is to produce an analytical structure that is flexible 

enough to enable users to employ a variety of modeling techniques that can be used with 

virtually any proposed wireless technology. The framework’s main components are a 

MAC sublayer model, a PHY model, a module that performs coverage analysis, a 

channel propagation model, and a model for multiple links (multi-link). The overall 

structure of the model is shown in Figure 9. The following subsections discuss each of 

these components and explain how they interact with each other and operate within the 

larger analytical framework. 
50 

Figure 9 - Wireless modeling framework building blocks 

5.2.1 Channel Propagation or Path Loss Models 

Channel propagation or path loss models provide a means for characterizing how 

different wireless deployment environments impact a communications signal propagating 

along the wireless path between a transmitter and receiver. Since the attenuation of the 

transmitted signal directly impacts the signal to noise ratio at the receiver, it is the 

characteristic of greatest interest to the wireless communications designer. Other 

important characteristics are shadow fading, small-scale or fast fading, and penetration 

loss. 

Signal attenuation is modeled through the quantity known as the path loss. It is important 

to recognize that a single path loss model cannot fully describe or predict path loss 

characteristics for all possible scenarios. Operating frequency and the characteristics of 

the deployment environment such as indoor, outdoor, urban, suburban, or rural; must be 

taken into consideration along with the location of the transmitter and receiver antennas 

relative to the obstacles that are likely to be encountered along the propagation path. In 

this section we look at various channel or path loss models that can be considered to 

predict path loss for terrestrial wireless networks. 

MAC /

Data Link 

Layer

(Layer 2)

Range & 

Coverage 

Analysis

Propagation 

Model

Multi-Hop 

Model

Physical 

Layer

(Layer 1)

Wireless Technology Inputs

Smart Grid Requirements 

Inputs

Smart Grid 

Requirement 

Outputs

Environment 

Characteristics

Frequency Band

Deployment Limits

Data Density & 

Latency 

Requirements

Message Size

Number of Devices

Path 

Loss

Tx-Rx 

Spacing

System Gain/Link Budget

Availability

SNR

Link Delay

Link Reliability

Link Throughput

SINR

Failure Probability

Delay/Reliability

Throughput

Routing 

Topology

Spectral Efficiency, FEC, etc.

Max Tx-Rx Spacing 

Base Station 

Requirements 

for Coverage, 

Capacity, & 

Latency
51 

5.2.1.1 Generic Path Loss Model 

The path loss quantity, PL, models the attenuation of the signal in terms of the fraction of 

the received power to the transmitted power measured at the antennas. The deterministic 

component of the path loss, PLd, is a function of the path distance, d, in meters between 

the transmitter and the receiver. The widely accepted model in the wireless propagation 

community predicts an exponential attenuation as a function of distance according to a 

path loss exponent, n0. In non-line of sight environments, however, the degree of 

exponential fading increases to n1 after a certain breakpoint distance, d1. The breakpoint 

path loss model below (shown on a dB scale) captures this relationship: 











+ >

≤

= +

0 10 1 0 1 10 1 1

0 10 0 1

dB,d dB,0

10n log )d/d( 10n log ),d/d( d d

10n log ),d/d( d d

PL )d( PL , 

where d1, in meters, is the breakpoint where the path loss exponent changes from n0 to n1, 

and PL0,dB is the reference path loss at d0 = 1 m, given by the following equation: 

PL0 = 20log10(2πd0/ λ); where λ = wavelength in meters 

The random component of the path loss (PLr,dB = Xs,dB + Xf,dB) is composed from two 

terms. The first term, Xs,dB, is referred to as shadow fading. It represents the deviation of 

the signal from its predicted deterministic model due to the presence of large obstructions 

in the wireless path. Obstructions may be buildings or cars in the outdoor environment or 

partitions or furniture in indoor environments. These objects have varying size, shape, 

and material properties which affect the signal in different ways. Xs,dB is modeled as a 

zero mean Gaussian random variable with standard deviation, σ, in dB, a log-normal 

distribution. The second term, Xf,dB, is referred to as small-scale or fast fading. It

represents the deviation of the signal due to the presence of smaller obstructions in the 

path which cause scattering of the signal or multipath. These signals then constructively 

and destructively recombine at the receiver. Xf can be modeled as a unit-mean gammadistributed random variable with variance 1/m (where m is the Nakagami fading 

parameter9) and Xf,dB = 10 log10(Xf ). The shadow fading and small-scale fading are 

assumed to be constant during the transmission of a frame, mutually independent, and 

independent of the fading occurring on other links. The complete path loss model, 

including both deterministic and random components, is given by: 

PLdB = PLd,dB + Xs,dB + Xf,dB = PLd,dB + PLr,dB

Figure 10 shows an example of the path loss model extracted from actual measured data 

points. The deterministic component in red is fit to the blue data points collected in an 

indoor-to-indoor residential environment at a center frequency, fc = 5000 MHz (5 GHz). 

 

9

 Small-scale or fast fading is also often modeled as a Rayleigh distribution in non-line of sight 

environments or Rician when a dominant signal is present. 
52 

The deviation of the data points from the line reflects the contribution of the random 

component. 

 

Figure 10 - Breakpoint path loss model for indoor-to-indoor residential environment 

at fc = 5000 MHz 

5.2.1.2 Indoor Path Loss Models 

Assessing wireless performance in indoor environments is important for Smart Grid 

HANs which will generally operate in one or both of the license-exempt frequency bands 

at either 2400 MHz or 5000 MHz (2.4 GHz or 5 GHz). In addition to the HAN, a 

wireless solution may also be considered for aggregating data from basement or ground 

level meter clusters in multiple dwelling units and then via an indoor-to-indoor path, 

provide a means for connecting to individual HANs in a multi-story building to complete 

the end-to-end HAN-to-utility communication link. 

As compared to outdoor networks, indoor networks for Smart Grid are characterized by: 

• Shorter distances: Typically less than 100 meters 

• Maximum BS or AP antenna heights constrained by ceiling heights: Typically 

3 m to 5 m for office environments and 2.5 m to 3 m in residential environments. 

• Lower antenna gains and lower transmit power to ensure EIRP is in compliance 

with FCC human exposure safety requirements10 [6]: Must be < 1 mW/cm2 for f 

> 1500 MHz and < f/1500 mW/cm2 for 0.30 MHz < f < 1500 MHz (see Figure 

11). For unlicensed spectrum, FCC Part 15.247 specifies a maximum EIRP of 

+30 dBm (1 watt)11. 

 

10 RF exposure considerations are necessary when antenna locations are subject to accessibility by 

members of the public. 

11 Commercially available off-the-shelf APs have EIRPs that generally fall in the range of 200 mW to 300

mW well below the 1 watt allowed. 
53 

• The use of license-exempt ISM bands for indoor venues will be subject to 

interference from other applications in close proximity; microwave ovens, garage 

door openers, cordless phones, private WiFi networks, etc. 

Smart Grid deployment requirements for indoor located BSs are: 

• Indoor BS or AP: 0.5 meters to 5 meters above baseline

• Indoor SSs / Terminals: 0.5 meters to 5 meters above baseline

• Special Situations: Basement to customer connections (HANs) in multi-level 

residential and commercial buildings. This would require installations that favor 

upward directing antennas beams.

Figure 11 - RF exposure limits and EIRP 

5.2.1.2.1 ITU-R M.1225 Indoor Model 

The ITU-R M.1225 recommendation [7] was developed for the purposes of evaluating 

technologies for IMT-200012 in one of the 2000 MHz bands. The indoor model is based 

on the COST231 indoor model. The ITU-R M.1225 variant includes an unspecified 

number of walls or partitions in an office environment and a term to specifically account 

for floor loss. Since the formulation is designed for 2000 MHz, there is no frequency 

dependent term. The assumed antenna height for the SS is 1.5 m. The formulation for 

non-LoS indoor path loss is: 

	  	   	

 	 . 

  	.

where 

d = path length in meters, 3 < d < 100 

 

12 IMT (International Mobile Telecommunications) -2000 is the global standard for third generation (3G) 

wireless communications as defined by the International Telecommunications Union. 
54 

nf = number of floors 

In applying this model, the ITU-R M.1225 recommended allowance for shadow fading is 

12 dB, a relatively large number. 

The COST231 indoor model on which the ITU-R M.1225 model is based is more general 

and has the form: 

	  	  ! 		 "

	 	#$% % 	  $ 

&'	(  	* &'	) 

where: 

PLfs = Free space loss 

Lc = A constant, normally set to 37 dB 

nw = Number of penetrated walls 

Lw = Loss per wall (3.4 dB for plasterboard internal walls and 6.9 dB for concrete or 

brick walls) 

Lf = Loss between floors (18.3 dB assumed for typical office environment) 

nf = Number of penetrated floors 

b = Empirically-derived parameter 

The expression for the free space path loss is given by: 

PLfs = 20log10(4πd/λ) = 20log10(d) + 20log10(f) – 27.56 dB; 

where: 

d is path length in meters and 

f is frequency in MHz 

5.2.1.2.2 WINNER II Indoor Model 

The WINNER II Indoor Model is defined for an indoor office building environment in 

which the BSs or APs are installed in corridors. Transmissions from corridor to specific 

offices represent the non-LoS case. The model is based on measured data primarily at 

2000 MHz and 5000 MHz. The formulation, which contains terms specifically for 

penetration through walls and floors, is: 

	  	43.8 + 36.8 log)2
3 	+ 20 log)2
6 5000 ⁄  	+ 	9 + 	17	 + 4$ 	− 1

where: 

d = path length in meters, 3 m < d < 100 m, and 

f = frequency in MHz from 2000 MHz to 6000 MHz 

nf > 0 is number of floors 

nw is number of walls the signal must pass through 

X = 5(nw – 1) for light walls and 12(nw - 1) for heavy walls. 

At 2000 MHz the WINNER II expression becomes: 
55 

	 = 	35.8 + 36.8 log)2
3 	+ 9 +	17	 + 4$ 	 − 1

The recommended allowance for shadow fading with the WINNER II indoor model is 

4 dB. 

WINNER II also provides a variation to the model for room-to-room transmissions. It is 

given by: 

	 = 	  !	 + 9	 +	17	 + 4$ 	− 1

where: 

X = 5nw dB for light walls and 12nw dB for heavy walls; and 

nw = the number of walls intersected by the signal. 

This formulation does not have a specific term to account for excess loss due to clutter 

loss or shadowing, but recommended allowance for shadow fading is 6 dB for light walls 

and 8 dB for heavy walls. 

5.2.1.2.3 ITU-R M.2135-1 Indoor Model 

The test environment described for which the ITU-R M.2135-1 indoor model applies is a 

single floor in a building with 16 rooms and a long hall, 120 meters long and 20 meters 

wide. The formulation for the ITU-R M.2135-1 indoor model is: 

PL = 11.5 + 43.3log10(d) + 20log10(f/1000) 

where: 

d = path length in meters, 10 m < d < 150 m, and 

f = frequency in MHz from 2000 MHz to 6000 MHz 

The path loss formulation has a higher loss dependency on distance which can be 

explained by the number of wall penetrations called for in the described test environment. 

The expression is considered valid for AP antenna heights from 3 m to 6 m and SS 

heights from 1 m to 2.5 m. Shadow fading of 4 dB is recommended in the ITU-R 

M.2135-1 testing methodology. 

5.2.1.2.4 NIST PAP02-Task 6 Model 

NIST conducted studies for indoor-to-indoor, outdoor-to-outdoor, and outdoor-to-indoor 

propagation paths13 [8][9]. In all cases the formulation presented in section 5.2.1.1 was 

fitted to the measured data, namely: 

PLd = PL0 + 10n0log10(d/d0) for d ≤ d1

PLd = PL0 + 10n0log10(d1/d0) + 10n1log10(d/d1) for d > d1

 

13 See also http://www-x.antd.nist.gov/uwb for more measurement details 
56 

In the following non-LoS deployment scenarios for indoor-to-indoor, do is assumed to be 

1 m and the remaining parameters are shown in the Table 414. The results, using the 

above formulations, are plotted in Figure 12.

Table 4: Parameters for indoor-to-indoor non-LoS deployment scenarios 

2400 MHz PL0 

(dB) n0

d1 

(m) n1

σ

(dB) 

Residential 12.5 4.2 11.0 7.6 3.0 

Office 26.8 4.2 10.0 8.7 3.7 

Industrial 29.4 3.4 1.0 3.4 6.3 

Cinder Block 9.1 6.9 1.0 6.9 6.7 

5000 MHz PL0 

dB n0

d1 

(m) n1

σ

(dB) 

Residential 20.2 4.4 11.0 7.4 3.3 

Office 26.0 4.3 10.0 10.1 4.0 

Industrial 27.5 3.7 1.0 3.7 6.7 

Cinder Block 7.8 7.3 1.0 7.3 7.7 

Figure 12 - Results for PAP02 Task 6 non-LoS indoor model 

Many of the measurements for the PAP02 Task 6 model were taken with transmitters and 

receivers located in hallways with measurement distances ranging from 5 m to 45 m. The 

graphs in Figure 12, therefore, are limited to the 5 m to 45 m range and assume the 

greater of free space loss or model-predicted path loss to eliminate the impact of waveguiding affects with hallway measurements. 

5.2.1.2.5 Indoor Model Comparison 

With the exception of the NIST PAP02 – Task 6 Model, the other three models are based 

on an office environment. The configurations used as the basis for the models differ thus 

 

14 The table in the cited reference only accounts for excess path loss, the value 2 is added to the path loss 

exponents in this case to provide a formulation for total path loss. 
57 

resulting in significant differences in the path loss predictions. The first difference to 

notice is the loss dependency relative to distance, ranging from 30 dB per decade for the 

ITU-R M.1225 model to 43.3 dB per decade for the ITU-R M.2135-1 model and up to 

87 dB per decade for the PAP02-Task 6 Office Model for d > 10 m. 

The WINNER II and ITU-R M.2135-1 indoor path loss models both assume that 

penetration losses between 2000 MHz and 6000 MHz are independent of frequency. 

Since these models are based on measurement data at 2000 MHz and 5000 MHz, this 

conclusion suggests that the indoor penetration losses are dominated by loss due to 

reflections as opposed to absorption losses in the wall material. Except for the residential 

case, the PAP02 – Task 6 model does predict an increase in excess loss with increased 

frequency as indicated by the increase in the parameter n1 at 5000 MHz. 

The four indoor models are compared at 2000 MHz in Figure 13. The plot for the 

WINNER II model is for corridor-to-room with a single light wall penetration. As a 

point of reference, the dashed line represents the free space path loss. 

Figure 13 - Comparison of four indoor path loss models for office environment 

Indoor path loss models will play a key role in coverage analysis for HANs and, although 

these models are based on office environments, they can be applied to residential 

environments using the predicted penetration loss for light walls: 3.9 dB (COST231) to 

5.0 dB (WINNER II) per wall. 

5.2.1.2.6 Modeling Floor-to-Floor Penetration Losses in Multilevel Buildings 

Meeting the challenge of connecting basement-located meter clusters to individual 

households and businesses in multi-level apartment and office buildings is of great 

interest to utilities. Getting a reasonably accurate prediction for floor-to-floor penetration 

loss is essential for assessing the performance limitations for this use case. 

Table 5 compares floor loss between the ITU and WINNER II indoor models and 

measurement data at 1900 MHz for three commercial office buildings [10][11]. The 

measured data includes, in parenthesis, the standard deviation for the multiple 
58 

measurements done in conducting the tests. Although there are differences between these 

and other floor loss projections found in the literature, most likely attributable to the 

varied design and materials in the buildings used for the measurements, they all predict a 

higher attenuation for the first floor penetration and a lower attenuation for additional 

floors. The data for building #3 in fact showed virtually no change in loss after the first 

floor penetration. The measurement results shown in Table 5 also indicate a reduced 

spread in the collected data with increased floor penetrations. Unfortunately no 

measurement data could be found for buildings beyond five (5) stories. 

The spread in the predictions between the two indoor path loss models for multiple floor 

penetrations is significant. Comparing the model predictions with the measured data at 

1900 MHz suggests that a better estimate for penetration loss beyond the first few floors 

lies somewhere between what the two models predict.

Table 5: Comparison of floor loss between the ITU and WINNER II 

Number of 

Floor 

Penetrations 

Measured Path Loss at 1900 MHz

[PL(σ)] Predicted Path Loss 

Building #1 

in dB 

Building #2 

in dB 

Building #3 

in dB 

ITU-R 

M.1225 

Model (dB) 

WINNER II 

Model (dB) 

1 31.3 (4.6) 26.2 (10.5) 35.4 (6.4) 18.3 17.0

2 38.5 (4.0) 33.4 (9.9) 35.6 (5.9) 33.5 21.0

3 35.2 (5.9) 35.2 (3.9) 43.6 25.0

4 38.4 (3.4) 51.1 29.0

5 46.4 (3.9) 57.1 33.0

6 62.2 37.0

Table 6 summarizes the key differences between three of the indoor path loss models 

discussed in this section. None of the models predict a difference in excess path loss with 

frequency. 

Table 6: Key differences in indoor models 

Indoor Model Frequency Path Loss 

Exponent Wall Loss Floor Loss 

ITU-R 

M.1225/COST231 

2000 MHz 3.0 3.4 dB to 6.9 dB 

per wall 

18.3 dB + 15 dB + 

10 dB + 7.5 dB 

WINNER II 2000 MHz to 

6000 MHz 

3.68 5 dB to 12 dB per 

wall 

17 dB + 4 dB per 

floor 

ITU-R M.2135-1 2000 MHz to 

6000 MHz 

4.33 Included in path 

loss exponent 

Not specified

5.2.1.2.7 Indoor Model Summary 

The differences in the predicted path loss for the four indoor models described in this 

section illustrate the limitations of the approach used to derive mathematical models. 

With indoor environments, it is especially difficult to identify a typical measurement 

environment from which to generate a mathematical model that would be generically 

applicable for either residential, office, or industrial environments. Factors such as 

building construction, types of materials, room layouts, along with the varied location, 
59 

amount, and types of furnishings greatly impact the path loss data. At some frequencies, 

wave-guiding effects with transmitters and receivers located in hallways can also 

decrease path loss to values less than free space loss. Additionally, measurement data is 

often taken with tripod-mounted equipment with antenna heights that may not represent a 

permanent deployment which would generally have APs mounted at ceiling height. 

Indoor measurements can also be affected by structures and furnishings located within 

the near-field region of the transmitting antenna. The combination of these factors 

greatly complicates the data analysis and the subsequent derivation of a generic indoor 

path loss model. 

Figure 13 can be used as a guide for judging which indoor model is most applicable for 

analysis and comparative purposes. The graph shows reasonably good correlation 

between the PAP02 Task 6, WINNER II, and ITU-M.1225 models for path lengths less 

than 15 meters whereas the two ITU models correlate quite closely for path lengths 

greater than 15 meters. Whichever indoor model is used it is important to be 

conservative in applying the predicted results for planning or estimating equipment 

requirements. In cases where unique environments are being considered, which may be 

the case for meter clusters in basement locations; it would be desirable to conduct on-site 

field tests to supplement the model predictions before committing to a permanent 

deployment. 

5.2.1.3 Large Scale Outdoor Path Loss Models 

In this section we look at a number of commonly used path loss models that can be 

considered for terrestrial “last mile” coverage analysis for assessing the suitability of 

wireless technologies for smart grid communications networks. All of these models have 

been derived from field measurements and, based on how and where the measurements 

were made, have some constraints that must be carefully considered before they are 

applied to any specific deployment scenario. The goal of this section is to provide a 

greater understanding of the benefits and limitations in using these models to predict total 

propagation path loss and ultimately provide an estimate for range and coverage for the 

wireless technology being considered for terrestrial wireless WAN, FAN, AMI, or 

backhaul deployments. 

For Smart Grid wireless communication last mile network analysis, utilities require path 

loss models for outdoor terrestrial applications that are easy to apply and meet the 

following requirements for outdoor located BSs: 

• Frequency Range: Path loss model must cover 700 MHz to 6000 MHz 

• BS Antenna Height Range: 7 meters to 100 meters, below and above roof top 

levels 

• Terminal or SS Antenna Height Range: Sub-grade to 2 meters above grade for 

exterior locations and 1.5 meters to 6.5 meters for interior locations for FANs and 

1.5 meters to 10 meters for WANs. 

• Special Situations: Terminals located in meter vaults, below grade, and in 

basement locations 
60 

• Rural Regions: Ranging from flat open areas to hilly or mountainous terrain with 

and without foliage 

• Suburban Regions: 1- to 3-story residential with some commercial 

• Urban Regions: Commercial and Industrial, large 1- to 4-story buildings, low 

foliage 

• Dense Urban Regions: High rise residential and enterprise buildings

For outdoor located BSs several commonly used path loss models will be looked at in 

some detail and compared to the above requirements. Additionally, models developed 

specifically for predicting attenuation due to foliage and propagation path obstacles will 

be presented. This will lead to a suggested modification to one of the path loss models to 

provide a single path loss model that more closely fits the above utility requirements for 

suburban and rural areas over the frequency range of interest. 

The large scale terrestrial models that will be reviewed are listed in Table 7. 

Table 7: Terrestrial models 

Path Loss Model Applicable Frequency Range 

Hata-Okumura 150 MHz to 1500 MHz

COST231-Hata 1500 MHz to 2000 MHz

WINNER II 2000 MHz to 6000 MHz

ITU-R M.2135-1 2000 MHz to 6000 MHz

450 MHz to 6000 MHz (for rural) 

Erceg-SUI (Stanford University Interim) 1800 MHz to 2700 MHz

For simplicity in this discussion we will ignore the standard deviation that would apply to 

each of these models to account for the spread in the actual measured data as compared to 

the curve fit for the derived formulae. This zero-mean, log-normally distributed term can 

be taken into account when determining the link budget in the form of fade margin, a 

topic discussed later in this section. The fade margin will account for both slow lognormal shadow fading and fast fading with a value selected to meet a specific link 

availability goal. 

For outdoor-to-indoor and indoor-to-outdoor propagation, building penetration loss must 

also be factored into the path loss or may be included in the link budget calculation. Both 

fading and penetration loss will be discussed further in following sections. 

5.2.1.3.1 Hata-Okumura Model 

Okumura’s model is one of the first large scale models developed for wide area 

propagation and coverage analysis. The Okumura model is based on experimental data 

collected in the 1960s in the city of Tokyo, Japan [12] in the 900 MHz band. In 1980 M. 

Hata developed an expression to fit the path loss curves derived by Okumura [13]. The 

formulation for the Hata-Okumura model which is considered applicable from 150 MHz 

to 1500 MHz is: 

For urban deployment the Path Loss in dB is given by: 
61 

=>?@	A 	 = 	B. CC	

+ .  	



− .  	

DE

 	− @
FE 	+	G. B	 − . CC 	

DEH	



a(Rh) = 8.29[log10(1.54Rh)]2 - 1.1, for 150 MHz < f ≤ 200 MHz for large city 

a(Rh) = 3.2[log10(11.75Rh)]2 - 4.97, for 200 MHz < f ≤ 1500 MHz for large city 

a(Rh) = (1.1log10(f)-0.7)Rh - (1.56log10(f)-0.8), for small to medium size city 

For suburban and open area deployments the path loss is given by PLsuburban dB and PLopen 

dB, respectively. 

!I*IJ*K&	LM 	 = 	 IJ*K&	N&	LM 	− 2 Olog)2 P

6

28QR

(

− 5.4

STU&	LM 	 = 	 IJ*K&	LM 	− 4.78Glog)2
6H( + 18.33 log)2
6 	− 40.94

where: 

d = path distance in km valid from 1 km to 20 km 

f = frequency in MHz 

Th = BS antenna height valid from 30 m to 200 m (must be higher than 

average roof top or hill height) 

Rh = SS or terminal antenna height from 1.0 m to 10 m. 

In addition to the limited frequency coverage, a significant limitation for 

the Hata-Okumura model is the requirement that the BS antenna height 

must be higher than the average building height in the coverage area. 

Within these constraints, the model has proven to be an effective 

planning tool for cellular networks in the lower frequency bands. 

5.2.1.3.2 COST231-Hata aka Modified Hata Model 

The COST231-Hata model represents an extension of the Hata-Okumura 

model to cover frequencies higher than 1500 MHz [14]. The COST231 

path loss model is considered valid from 1500 MHz to 2000 MHz and 

has been used extensively to analyze coverage for mobile 

communications in the 1900 MHz band. 
62 

The COST231-Hata model, with a slight modification15, is specified in 

the 3GPP2 evaluation methodology for CDMA2000 [15]. The 

formulation for the COST231-Hata path loss model is given by: 

LM 	 = W	 + X log)2
6 	− 13.82 log)2
YZ

 	− [
\Z

+ G44.9	 − 6.55 log)2
YZ	Hlog)2
3 	+ 0.7\Z 	+ ]

where: 

d = path length in km 

f = frequency in MHz from 1500 MHz to 2000 MHz 

A = 46.3 

B = 33.9 

Th = BS antenna height from 30 m to 200 m (must be higher than 

average roof top height) 

Rh = SS antenna height from 1.0 m to 10 m 

For Urban Environments: 

[
\Z

 	 = 3.2Glog)2
11.75\ZH(

	− 4.97

and C = 3 dB 

For Suburban Environments: 

[
\Z

 	 = 	G1.1 log)2
6 − 0.7H\Z −	G1.56 log)2
6 	− 0.8H

and C = 0 

The limitations of the COST231-Hata model are similar to the HataOkumura model, namely, limited frequency coverage and the 

requirement that BS antenna heights must be above surrounding roof 

tops. 

5.2.1.3.3 WINNER II Model 

The WINNER II project, initiated in 2006 as an extension to WINNER I, 

is a consortium focused on technologies for IMT-2000. One key output 

of this effort is the development of path loss models covering the 

frequency range from 2000 MHz to 6000 MHz using a combination of 

information available in the literature and applicable measurements 

contributed by the consortium members. The output is a collection of 

 

15 The path loss is reduced by 3 dB from the COST231-Hata prediction for the purposes of the 3GPP2 

evaluation methodology 
63 

models for both LoS and non-LoS for both indoor and outdoor venues 

[16]. 

The following three variants of the WINNER II models are selected for 

description in this section. 

C2 – Urban macro-cell, non-LoS: 

PLdB = [44.9 – 6.55log10(Th)]log10(1000d) + 34.46 + 5.83log10(Th) + 23log10(f/5000) 

C1 – Suburban macro-cell, non-LoS: 

PLdB = [44.9 – 6.55log10(Th)]log10(1000d) + 31.46 + 5.83log10(Th) + 23log10(f/5000) 

D1 – Rural macro-cell, non-LoS: 

PLdB = 25.1 log10(d) + 55.4 – 0.13 log10(Th-25) log10(d/100) – 0.9 log10(Rh-1.5) + 21.3 

log10(f/5000) 

where: 

d = path length in km 

f = frequency in MHz from 2000 MHz to 6000 MHz 

Th = BS antenna height in meters from 25 m to 100 m (higher than roof top height) 

Rh = terminal antenna height in meters for > 1.5 m 

5.2.1.3.4 ITU-R M.2135-1 Model 

ITU-R M.2135-1 provides recommendations for IMT-Advanced16 and specifically 

lays out the guidelines for the IMT-Advanced technology evaluation methodology 

[17]. It has been adopted by both LTE and WiMAX / IEEE Std. 802.16 as an 

evaluation methodology. The path loss models adopted for ITU-R M.2135-1 are 

based on the WINNER II path loss models. 

As with WINNER II several deployment scenarios are defined, each with specific 

recommendations for BS and terminal antenna heights. The ITU-R M.2135-1 

formulation requires two additional parameters ( average building height and 

average road width ) thus making it somewhat more difficult for city to city 

comparisons. Average road width provides a means to indirectly infer building 

density. 

Since the values for building height and average road width can be used to differentiate 

between urban, suburban, or rural macro-cells, a single formulation applies for all three 

demographic scenarios. Recommended values for building heights, road widths, and 

antenna heights for each geographic area are provided for the purposes of IMT-Advanced 

technology evaluations but the formulation is considered valid for a wide range of 

building heights and road widths. The ITU-R M.2135-1 formulation is: 

 

16 International Mobile Telecommunications - Advanced (IMT-Advanced), aka 4G, defines a global 

platform for mobile systems that include the new capabilities of IMT that go beyond those of IMT-2000.
64 

LM 	 = 161.04	 − 7.1 log)2
^ 	+ 7.5 log)2
_ 	− 	`24.37	 − 3.7 P_

YZ

Q

(

a log)2
YZ



+ 	 
43.42	 − 3.1 log)2
YZ


log)2
10003 	− 3 + 20 log)2 P

6

1000Q 	

− 	 
3.2
log)2
11.75\Z

(

	− 4.97

Where: 

d = path length in km 

f = frequency in MHz applicable from 2000 MHz to 6000 MHz for urban and suburban 

environments and 450 MHz to 6000 MHz for rural environments 

W = average road width in meters from 5 m to 50 m 

H = average building height in meters from 5 m to 50 m 

Th = BS antenna height in meters from 10 m to 150 m (must be above average building 

height) 

Rh = terminal or SS height in meters from 1 m to 10 m

Although this model accommodates lower BS antenna heights, as with the previous 

models the BS antenna height must still be above the surrounding roof tops. There is 

another variant of the ITU-R M.2135-1 model however, that does support BS antenna 

heights below roof tops. 

Described as Urban Micro-cell, this model is based on a Manhattan-like grid layout 

specifically for BS antenna heights well below the roof tops of surrounding buildings. 

The effective coverage area for this scenario is defined by signals propagating along 

streets on which the BS is located and diffracting around the corners of buildings along 

streets that are perpendicular as illustrated in Figure 14. Except for blockages due to 

passing vehicles, outdoor SSs along the street on which the BS is located will be mostly 

LoS while outdoor SSs on perpendicular streets will receive signals diffracted around the 

corners of buildings. These signals will typically be stronger than signal components 

penetrating through the buildings to reach the same end-point. This model also includes 

a formulation to cover outdoor-to-indoor paths which would be of greatest interest for 

Smart Grid FAN applications.
65 

Figure 14 - Various transmission paths for urban micro-cell 

For non-LoS outdoor, assuming a hexagonal cell layout, BS antenna height at 10 meters, 

SS antenna height from 1 m to 2.5 m, and a street width of 20 meters, the formulation is: 

PLdB = 36.7log10(d) + 22.7 +26log10(f) 

For: 10 m < d < 2000 m and 2000 MHz < f < 6000 MHz 

For the outdoor-to-indoor scenario, the channel model comprises an outdoor component, 

an indoor component, and a value for penetration loss which, in general, is dependent on 

the angle of incidence to the building. For an unspecified angle of incidence, the building 

penetration loss is assumed to be 20 dB. 

The formulation, assuming a hexagonal cell layout, BS antenna height of 10 m, and an SS 

antenna height between 1 m and 2.5 m is: 

PLdB = 20 dB + PLout + PLin

For the outdoor component the distance is defined as the distance from the BS to the wall 

next to the indoor terminal and the distance for the indoor calculation is assumed to be 

evenly distributed between 0 m and 25 m (i.e., 12.5 m). 

5.2.1.3.5 Erceg-Stanford University Interim (SUI) Model 

The Erceg model is a statistical path loss model based on propagation data collected in 95 

different suburban environments throughout the United States at or close to a frequency 

of 1900 MHz [18][19]. To cover the range of encountered terrain and foliage 

characteristics for the data analysis, the environments were broken down into the 

following terrain categories. 

• Terrain Type A: Hilly with moderate to heavy tree density. 
66 

• Terrain Type B: Hilly with light tree density or flat and moderate to heavy tree 

density. 

• Terrain Type C: Flat with light tree density. 

The time of year was such that in most of the test locations leaves were on the trees, thus 

representing a worse case path loss scenario. BS antenna heights were in the range of 

12 m to 79 m. 

This model is especially interesting for Smart Grid network applications in that it is based 

on measurements taken in areas throughout the United States representative of rural and 

suburban areas of interest to the utilities companies at BS antenna heights close to what 

utility requirements have specified. 

The formulation for the Erceg-SUI model is: 

LM 	 = 20 log)2 P

4b32

c

Q 	+ 10 P[	 − dYZ 	+ 	

e

YZ

Q log)2 P

3

32

Q	+ 6 log)2 P

6

2000Q 	

− 9 log)2 P

\Z

2

Q

where: 

Th = BS antenna height in meters, 

Rh = terminal or SS antenna height in meters, 

d0 = 100 meters, 

λ = wavelength in meters, 

f in MHz, and 

d in meters. 

The remaining parameters are terrain dependent and defined in Table 8. 

Table 8: Parameters for terrain types 

Parameter Terrain Type A Terrain Type B Terrain Type C 

a 4.6 4.0 3.6

b 0.0075 0.0065 0.005

c 12.6 17.1 20

X 10.8 10.8 0

5.2.1.3.6 Comparing Large Scale Path Loss Models to Smart Grid Requirements 

All of the large scale outdoor models discussed have limitations with respect to meeting 

the deployment requirements for Smart Grid applications that were outlined in section 

5.2.1.3. No single model as described covers the entire frequency band of interest thus 

necessitating the need to apply at least three different path loss models to evaluate 

spectrum differences over the desired 700 MHz to 6000 MHz frequency range. This can 
67 

be an issue for technology comparative purposes since it is not assured that any two 

models will produce a similar result at a frequency considered valid for the two models. 

Other limitations of these models are summarized in Table 9. 

Table 9: Path loss models' limitations 

Path Loss Model Limitations Smart Grid Requirements 

Hata-Okumura

150 MHz to 1500 MHz 

-BS antenna height ≥ 30 m and

above roof tops 

- Favors urban / suburban 

environments 

- Limited frequency coverage 

-BS antenna height from 7 

meters to 100 meters above and 

below roof top heights 

- Urban, suburban, rural (with 

foliage, hills, and valleys) 

- Applicable from 700 MHz to 

6000 MHz 

COST231-Hata

1500 MHz to 

2000 MHz 

-BS antenna height ≥ 30 m and

above roof tops 

- Limited frequency coverage 

WINNER II

2000 MHz to 

6000 MHz 

-BS antenna height ≥ 25 m and

above roof tops 

- Limited frequency coverage 

ITU-R M.2135-1

2000 MHz to 

6000 MHz 

450 MHz to 6000 MHz 

(For rural) 

-BS antenna height must be 

above roof tops 

- Limited frequency coverage 

for urban and suburban 

ITU-R M.2135-1

Urban Micro-cell 

-BS antenna height fixed at 10 

meters 

- Limited range for SS antenna 

height 

- Manhattan-like grid structure 

- Limited frequency coverage 

Erceg-SUI

1800 MHz to 

2700 MHz 

-BS antenna height ≥ 10 m

-Based on suburban / rural 

measurements 

- Limited frequency coverage 

To specify or recommend a model to meet Smart Grid requirements it will be necessary 

to develop a new model based on extensive field measurements in varied environments or 

consider modifications to one of the existing models to increase its applicability. For the 

latter approach we have to look at some additional path loss models. 

5.2.1.3.7 Path Loss Due to Foliage 

Accurately predicting propagation path excess loss due to foliage, as has been pointed out 

in numerous studies, is a complex process. Based on information reported several 

conclusions can be drawn with respect to path loss due to foliage. 

• Vertically polarized signals experience higher attenuation than horizontally 

polarized signals in lower frequency bands 
68 

• Increases with frequency 

• Does not increase linearly with depth of foliage 

• There is a limiting value since signals will diffract around foliage 

• Is dependent on type of tree or foliage; a 3:1 range in attenuation coefficient was 

found in a University of Texas study [20] 

• Higher attenuation when trees are fully leaved 

• Higher attenuation when trees are wet 

Despite the above variations that complicate the adoption of a single universally 

applicable model, attempts have been made to derive closed form expressions to 

characterize excess path loss due to foliage [21]. 

Three easy to apply models for excess loss due to foliage (Lf

 in dB) are [22], [23]: 

• Early ITU model:  	 = . . 	f 	

.

• Optimized or fitted ITU-R (FITU-R) Model for foliage in leaf:

 	  . B.B 	f 	

.C

• Weissberger model [24]: 

 	  . . 	f	

. for df ≤ 14 m

	  . . 	f 	

.C for 14 m < df ≤ 400 m 

where: 

f is in MHz and 

df is the depth of foliage in meters. 

Figure 15 provides some comparisons between these three models over the spectrum 

of interest and for foliage depths of 50 m and 150 m. Figure 16 shows the foliage 

loss predicted by Weissberger’s model for foliage depths up to 400 m. 

Figure 15 - Comparison of foliage models 
69 

Figure 16 - Foliage loss predicted by Weissberger’s model 

5.2.1.3.8 Path Loss Due to Path Obstructions 

Except for the Erceg-SUI Model, all of the large scale path loss models discussed above 

are based on scenarios for which the BS antenna height is at or above surrounding roof 

tops thus avoiding the possibility of obstacles blocking the signal path prior to diffracting 

over roof edges for coverage at street level as illustrated in Figure 17. 

Figure 17 - Diffraction over roof tops for street level coverage 

Over the years numerous models and algorithms have been developed with varied 

complexity to predict the path loss due to terrain obstacles. The Epstein-Peterson 

Diffraction Model, presented in this section, appears to be a reasonable compromise 

between prediction accuracy and ease of use [25]. 

The formulation for diffractive loss, (Ld in dB), due to an obstruction is as follows: 

Ld (in dB) = L(v,0) + L(0,p) + L(v,p) 

Where: 

 L(v,0) = 6.02 + 9.0v + 1.65v

2 ; for -0.8 ≤ v ≤ 0 

 L(v,0) = 6.02 + 9.11v - 1.27v

2 ; for 0 < v ≤ 2. 

 L(v,0) = 12.953 + 20log(v); for v > 2. 
70 

and 

 L(0,p) = 6.02 + 5.556p + 3.418p

2

 + 0. 256p

3

and 

 L(v,p) = 11.45vp + 2.19(vp)

2

 - 0.206(vp)

3 - 6.02; for vp ≤ 3 

 L(v,p) = 13.47vp + 1.058(vp)

2

 - 0.048(vp)

3 - 6.02; for 3 < vp ≤ 5 

 L(v,p) = 20vp - 18.2 ; for vp > 5 

g	 = 0.676\2.hhh f	62.)iijk

3


3)3(

where 

R = obstacle radius in km, 

f in MHz, and 

d = d1 + d2

v = h [2 d/(λd1d2)]0.5 = h[fd/(150d1d2)

0.5] ; 

where 

f is in MHz, 

h is the obstruction height in meters, and 

d in meters 

For R = 0 (denoting knife-edge); L(0,p) = L(v,p) = 0, and Ld = L(v,0) 

Figure 18 for diffraction loss assumes a 500 m path length and path obstructions of 0.5m, 

1.0 m, and 2.0 m. 

Figure 18 - The Epstein-Peterson diffraction model 

For multiple path obstructions, each obstruction is treated separately and then added to 

yield the total path excess loss due to obstructions. This is illustrated in Figure 19. 
71 

Figure 19 - Accounting for multiple terrain obstructions 

5.2.1.3.9 Modified Erceg-SUI Model 

Although any of the large scale path loss models described to this point may be selected 

and applied to a specific smart grid use case under conditions that fit the constraints of 

the model being used, the more extreme smart grid requirements cannot be met with the 

formulations as they are described. The Erceg-SUI model comes closest to meeting the 

stated goals at least for suburban and rural regions, since the testing environments did in 

fact include foliage and hilly terrain in conjunction with relatively low BS antenna 

heights. However, as is also the case for the other path loss models, the frequency range 

for which the Erceg-SUI is considered valid is limited to a small portion of the required 

700 MHz to 6000 MHz range. 

Further study of the Erceg-SUI path loss expression suggests that a simple modification 

to the term that determines the sensitivity of excess loss to frequency can increase the 

applicability of the Erceg-SUI model over a broader frequency range. The proposed 

modification is as follows: 

• The term, 6 log10(f/2000), is modified17 to: 6(1 + ak/Th) log10(f/2000). 

For k > 0 this will have the effect of increasing the excess loss frequency dependency 

without altering the path loss at 2000 MHz, the frequency at which the original data was 

collected. The modification also results in a frequency dependency that is greater with 

lower BS antenna heights as would be expected, since the impact of foliage and losses 

due to obstacles will be more significant with lower antenna heights. The resulting 

formulation for total path loss is then: 

PLdB = 20log10(4π d0 /λ)+10(a-bTh+c/Th)log10(d/ d0) + 6(1 + ak/Th) log10(f/2000) - 

Xlog10(Rh/2) 

 

17 This modification was arrived at after discussions with Vinko Erceg one of the principal investigators 

involved with the testing and derivation of the Erceg-SUI path loss model. 
72 

Table 10 shows the resulting excess loss frequency dependency, referenced to 2000 MHz, 

in dB per octave, for k = 4. The row with k = 0 represents the excess loss frequency 

dependency for the original Erceg-SUI formulation. The proposed modification results in 

an excess loss dependency on frequency, relative to 2000 MHz, that increases with lower 

BS antenna heights. This is consistent with the expectation that excess loss due to foliage 

and terrain obstacles would be more significant with lower antenna heights. For 

comparative purposes the following table includes the excess loss frequency dependency 

for the other large scale terrestrial path loss models discussed thus far. 

Table 10: Model and its path loss dependence in dB per octave 

Path Loss Model k Th

PL Frequency Dependence in dB/octave 

Type A Type B Type C 

Erceg-SUI 0 Any 1.81 dB 1.81 dB 1.81 dB

Modified Erceg-SUI 

4 80 m 2.22 dB 2.17 dB 2.13 dB

4 50 m 2.47 dB 2.38 dB 2.33 dB

4 30 m 2.91 dB 2.77 dB 2.67 dB

4 10 m 5.13 dB 4.70 dB 4.41 dB

Urban Suburban Rural 

Hata-Okumura 30 m 1.85 dB 1.38 dB

COST231-Hata 30 m 4.18 dB 3.71 dB

WINNER II 25 m 0.9 dB 0.9 dB

ITU-R M.2135-1 25 m 0.0 dB 0.0 dB 0.0 dB

To test the validity of this modification of the Erceg-SUI model over a wider range of 

frequencies, a comparison is made with the excess loss predicted by the modified ErcegSUI model for a 1 km path length with excess loss predicted by the Weissberger foliage 

model and the Epstein-Peterson diffraction model for a 175 m foliage depth and single 2 

m path obstruction, respectively. 

Figure 20 - Foliage and diffraction loss compared to modified Erceg-SUI model 

As Figure 20 illustrates, this proposed modification to the Erceg-SUI path loss model 

provides a reasonably close match to what is predicted by foliage loss based on the 
73 

Weissberger model or obstruction loss based on the Epstein-Peterson model or, 

alternatively, a combination of the two. 

5.2.1.3.10 Model Limitations with Respect to Meeting Smart Grid Deployment 

Requirements 

In the previous sections several large scale path loss models for terrestrial applications 

have been discussed. It was shown that a modified version of the Erceg-SUI model could 

be applied for suburban and rural environments over the desired frequency range with a 

range of BS antenna heights consistent with Smart Grid deployment requirements. 

Identifying a suitable path loss model for urban areas proved far more challenging. Three 

different models are necessary to cover the spectrum requirements and no solution was 

found to be valid for BS antenna heights below the surrounding roof top heights in the 

700 MHz to 2000 MHz band. Although there are multiple options that are considered 

valid for analyzing urban regions with BS antenna heights above neighboring building 

heights, care must be exercised when analyzing the data since, despite similar parameter 

assumptions, the range predictions will not be exactly the same. It is especially important 

when comparing multiple wireless technologies that the same path loss model be used 

with each of the technologies. For example, using the Hata-Okumura model at 

1500 MHz for Technology A and COST231-Hata at 1500 MHz for Technology B will 

not be a fair comparison because the differences in the models will mask any differences 

that exist between the two wireless technologies. 

Table 11 provides a summary for the large scale terrestrial path loss models discussed in 

the preceding sections. 

Table 11: Summary of large scale terrestrial path loss models 

Deployment Area 700 MHz to 

1500 MHz 

1500 MHz to 

2000 MHz 

2000 MHz to 

6000 MHz 

Urban Area with BS 

antenna above 

average roof top 

height 

Hata-Okumura COST231-Hata WINNER II or ITUR M.2135-1: Either 

model can be used. 

The ITU model 

provides a more 

conservative range 

estimate and takes 

building height and 

density into 

consideration. 

Both of these models have been used 

extensively over the years. Be aware however, 

the range predictions differ considerably at 

1500 MHz, where they are both considered 

valid models. At 2000 MHz there is 

reasonably good correlation between 

COST231-Hata and the WINNER II and ITUR M.2135-1 models. 
74 

Deployment Area 700 MHz to 

1500 MHz 

1500 MHz to 

2000 MHz 

2000 MHz to 

6000 MHz 

Urban Area with BS

antenna at 10 m or 

less 

There does not appear to be a proven solution 

in these frequency bands for BS antenna 

heights below surrounding building heights. 

ITU-R M.2135-1 

Urban Micro-Cell: 

Although specifically 

defined for a 

Manhattan-like grid 

structure and fixed BS 

antenna height of 10 

m, this model should 

be applicable in most 

urban centers 

Most Suburban or 

Rural areas with BS 

antenna heights from 

7 m to 80 m 

Modified Erceg-SUI Model: This model was shown to be generally 

applicable to a wide range of suburban or rural deployments at BS 

antenna heights ranging from less than 10 m to 80 m over the entire 

700 MHz to 6000 MHz frequency range. 

Extreme Rural 

Terrain 

Epstein-Peterson Diffraction Model or Weissberger Foliage Model: 

These models can be used together or individually in conjunction with 

free space path loss predictions for more extreme rural terrain 

conditions. Not an ideal approach for PMP but can be a very effective 

approach for PtP deployments. 

5.2.1.3.11 Modeling Extreme Terrain Characteristics 

In the previous sections we have looked at five different, frequently used, large scale path 

loss models that have been developed for analysis of terrestrial wide area wireless 

networks in urban, suburban or rural areas. Additionally we have discussed specific 

models for excess loss due to foliage and diffractive loss due to terrain obstacles. Using 

the Erceg-SUI model as a basis, a modification to the formula has been proposed to 

improve the applicability of this model over a broader frequency range in suburban and 

rural environments with varied terrain and foliage characteristics. 

From time to time it may be necessary, for rural areas, to estimate path loss for extreme 

propagation path conditions that do not appear to fall within the Erceg-SUI Type A 

terrain characteristics. An alternative approach for extreme conditions is to identify the 

worst case path conditions for a specific link within the desired coverage area and use 

GIS data, or equivalent, and apply the foliage model, terrain obstacle model, or both 

models to determine excess path loss for the specific path under consideration. Adding 

this value to the free space loss provides an estimate for the total path loss for the worse 

case link. Other models generally used for point-to-point links, such as the Egli [26] or 

Longley-Rice models [27], can also be considered. 

5.2.1.4 Atmospheric Absorption 

The question of atmospheric absorption is also often raised with respect to propagation. 

Fortunately for terrestrial WAN or FAN deployments in the frequency bands of interest 

and the typical path lengths encountered, atmospheric absorption is not significant. The 

anticipated losses are shown in Figure 21 derived from the formula developed in [28]. 

The plot for water absorption assumes 100 % humidity at 30 oC.
75 

Figure 21 - Atmospheric absorption from 1 GHz to 100 GHz 

Although atmospheric absorption can generally be ignored for terrestrial applications in 

the frequency bands below 6 GHz (6000 MHz), it can be a performance factor in 

frequency bands above 10 GHz and with the longer path lengths that would be typical 

when satellite technologies are being considered for smart grid communication solutions. 

5.2.1.5 Line of Sight (LoS) and Fresnel Zone Clearance 

Backhauling DAPs and other remotely located sites will often require the use of point-topoint links and in some cases multiple or daisy-chained links. Making use of existing 

utility poles can prove to be a cost-effective means for establishing point-to-point links. 

There are no right-of-way issues and foliage is generally cleared along these routes so as 

not to interfere with the power lines, LoS or near-LoS is therefore, assured. Relative 

antenna heights, however, are still important and can be a major factor in the path loss 

estimation. This is one application where the use of higher frequencies may prove to be 

an advantage. 

For true LoS the propagation path must be clear of obstacles for a distance equal to or 

greater than the first Fresnel Zone18 (see Figure 22). In practice a general guideline is to 

assure that at least 60 % of the first Fresnel Zone is clear of obstructions. 

 

18 The Fresnel Zone is an ellipsoid stretching between the transmit antenna and the receive antennas. The 

first Fresnel Zone is defined as the locus of points such that the indirect signal path is 180 degrees out of 

phase with the direct signal path. 
76 

Figure 22 - 1st Fresnel zone for point-to-point link 

An expression for the first Fresnel Zone, F1, is given by: 

l) 	 = 17.3k


3)3(


6m

where 

d1 + d2 = D, the path length and 

f is frequency in GHz 

As shown in Figure 23, for Tx and Rx antenna heights at 10 meters the earth represents 

an obstacle for well over 60 % of the first Fresnel Zone at 700 MHz. In this scenario 

transmitted vertically polarized19 multipath reflections from the ground will arrive at the 

receive antenna in such a way so as to detract from the direct signal thus creating excess 

path loss. At the higher frequencies there is considerable clearance for the first Fresnel 

Zone and reduced likelihood of out-of-phase reflections. Note that at longer path lengths 

the Earth’s curvature must also be taken into account when analyzing antenna height 

requirements for Fresnel Zone clearance. 

 

19 Horizontally polarized signals will reverse phase on reflection and actually add to the direct signal, this 

however is not something to be relied upon as ground reflections, except over water, are not predictable. 
77 

Figure 23 - Fresnel zone is wider at lower frequencies 

5.2.2 Range and Coverage Analysis 

The purpose of the coverage analysis is to predict the maximum range of a wireless 

technology for a given outage probability and a specified set of operating parameters. 

The range capability of a wireless technology helps determine its suitability for linking a 

particular pair of actors and predicts its coverage area in a point-to-multipoint topology. 

The outage criterion is the probability that the wireless transmitter-receiver link is not 

operational. It is expressed in terms of a probability due to the unpredictable behavior of 

RF propagation. It is often modeled as a stochastic process when accounting for the 

possible losses due to obstructions (shadowing) and reflections (multipath fading). 

In the context of a point-to-multipoint wireless technology, coverage can be analyzed in 

terms of the maximum cell radius that a BS or AP can support. Within the cell coverage 

area, the outage probability varies, generally increasing for terminals / actors located at or 

near the cell edge. The outage criterion is expressed in terms of the average outage 

probability, averaged over all locations within the cell coverage area. A reported outage 

probability of 1 %, for example, means that a terminal located at a random point in the 

cell has a 1 % chance of being in outage. We define the outage probability as the 

probability that the received signal to interference plus noise ratio (SINR) is below the 

required SINR to operate the link. The required SINR depends on the wireless 

technology under consideration and serves as an input for the analysis. With a known 

transmit power the received SINR can be estimated by using the appropriate path loss 

model described in section 5.2.1 together with suitable margins for fading, interference, 

and when applicable, penetration loss. 

5.2.2.1 Link Budget 

A Link Budget analysis accounts for all of the relevant network parameters and thus 

serves as an essential tool in the analysis and design of a wireless network. 
78 

Control channels and data channels in wireless networks often use different features. 

Therefore the system gain and hence the link budget for control channels and data 

channels tend to be different. For example, during the network entry procedure when the 

bulk of the control messaging is exchanged in a wireless network, several features that 

enhance the link budget may not be used. These features are available however, for the 

data channels. These link budget enhancing features include: Hybrid Automatic Repeat 

Request (HARQ), MIMO, Beamforming, etc. 

The system gain (SysGain) and link budget (LB) must be calculated for the data channels 

and the control channels for both uplink (UL) and downlink (DL) traffic. The applicable 

link budget for projecting the range is the minimum of: DL Control Channel LB, UL 

Control Channel LB, DL Data Channel LB, and the UL Data Channel LB. 

To calculate the various link budgets, the following parameters are required: 

a) Effective Isotropically Radiated Transmit Power in dBm (TxEIRP) 

b) Receiver Sensitivity at lowest desired operating modulation and coding in 

dBm (RxSNS) 

c) Combining Gains (HARQ gains, repetition gain, etc.) in dB (CombGain) 

d) Receiver Antenna + Amplifier gain in dB (RxGain) 

e) Receiver Cable Loss in dB (CablLoss) 

f) Fade Margins (Fm) to account for fades due to Shadowing and Multipath 

g) Interference Margin (Im) must include margin for both self-interference and 

inter-operator interference 

h) Penetration Loss (Lp) when applicable for indoor to outdoor or outdoor to 

indoor paths 

The combination of items a) thru e) is generally referred to as the System Gain and is 

given by: 

SysGaindB = TxEIRPdBm – RxSNSdBm + CombGaindB + RxGaindB – CablLossdB

When determining the receiver sensitivity, RxSNSdBm , in either the uplink or downlink 

direction, it is important to carefully consider the required throughput requirements for 

devices located on the cell edge. With knowledge of the PHY and media access control 

overhead, (PHY-OH + MAC-OH), for the specific technology being considered, the 

acceptable cell edge modulation efficiency and code rate can be determined to provide a 

required Eb/No and SNR to meet the desired cell edge performance.

The Link Budget (LB) represents the maximum allowable path loss for acceptable 

performance for a specific channel at the cell edge (MaxPL ) and is given by the 

System Gain minus the margins allowed for fading, interference, and penetration loss. 

LB = MaxPL = SysGaindB – Fm – Im - Lp 
79 

The maximum system allowable path loss is given by the minimum MaxPL for all 

channels 

MaxPLsys = min (MaxPL over all channels in either UL or DL direction) 

For an all-outdoor system, fading is generally the dominant variable for assessing 

probability of an outage. For a predefined system, the outage probability at a certain 

distance (d) from the BS or AP can be calculated as follows: 

Fade Margin = MaxPLsys – PL (d) – Im

where 

PL (d) is the path loss at a distance, d, as calculated by one of the path loss models in 

section 5.2.1. 

Assuming a certain dominant fading profile for an environment, (Log-normal, Rayleigh, 

or Rician), the outage probability is given by: 

Outage Probability = Probability (Random Fading > Fade Margin) 

The above analysis can be done in reverse to calculate the maximum allowable range or, 

for ubiquitous coverage with a multi-cellular deployment, the maximum allowable BS to 

BS spacing to guarantee a specific outage probability. 

Both the System Gain and Link Budget are closely linked to the smart grid use case that

is being analyzed. Outdoor BS parameters for terrestrial wide area deployments are 

relatively independent of the Smart Grid use case. Typically these systems will be 

capable of transmitting at the maximum EIRP allowed by regulatory restrictions for the 

frequency band of operation and most solutions will support the many advanced antenna 

technologies supported by the applicable standard. There may be some exceptions for 

mixed deployment scenarios combining macro, micro, and pico-cells where BS EIRP 

limitations may be necessary to help manage inter-cell interference. For indoor 

deployments, as mentioned in section 5.2.1.2, BS EIRP limitations would generally be 

required to comply with human exposure safety requirements. 

In contrast to the BS the terminal or SS characteristics will vary considerably depending 

on its role in the Smart Grid network. The terminal or actor location can also have a 

significant impact on the link budget and path loss. Since terminals will almost always 

be more limited in EIRP due to antenna and transmit power constraints and in some cases 

human exposure safety limitations, the UL system gain and link budget will generally be 

the limiting factor for range predictions. 

Wireless terminals applicable to a variety of Smart Grid use cases can be described as 

follows: 

• Fixed Outdoor-Mounted Terminal: This would be a typical installation for a 

DAP, substation, feeder line device, or other distribution or transmission facility. 

The terminal or SS can be mounted on an existing utility pole or transmission 
80 

tower, on top of or on the side of an existing structure, or on an existing 3rd party 

tower. For this type of installation the terminal can be equipped with a high gain 

directional antenna that is aligned relative to the BS to maximize received signal 

strength. With easy access to an alternating current power system and an antenna 

location not easily accessible by the general public, the uplink transmit power 

(TxEIRP) can be set to any level up to the maximum allowed by regulation. In 

summary this application is characterized by: 

o High Terminal Antenna Gain: Typically 12 dBi to 17 dBi dependent on 

operating frequency and antenna size 

o High Transmit Amplifier Power: FCC regulatory EIRP limits range from 

43 dBm to 85 dBm in licensed bands between 700 MHz and 6000 MHz 

o Relatively High Antenna Height: Typically 8 m to 10 m or higher 

• Vehicular-Installed Mobile Terminal: Equipping utility emergency vehicles 

with mobile wireless stations can provide a key communications link for disaster 

recovery, as well as routine grid maintenance activities. Compared to the Fixed 

Outdoor Terminal, these installations are characterized by:

o Lower Antenna Gain: Must be omni-directional in azimuth, typically 

6 dBi to 8 dBi 

o Lower Antenna Height: Typically 2 m to 3 m, if mounted on vehicle roof 

o Lower Transmit Power: Must comply with human exposure safety 

requirements 

• Fixed Indoor Self-Install Terminal: In a Smart Grid network this would apply 

to a remote office, a temporary quick-to-install station, or possibly a work-athome situation for a key utility employee. For this application the link budget is 

impacted by: 

o Antenna Gain: limited in size for convenience purposes, typically 6 dBi to 

8 dBi 

o Antenna Height: Typically 1 m to 3 m 

o Lower Transmit power (EIRP): Must comply with human exposure safety 

requirements 

o Building / Wall Penetration Loss: This can vary from 3 dB to 4 dB for a 

window-placed terminal in the 700 MHz band to more than 15 dB to 

20 dB for a location well inside an urban building in the higher frequency 

bands. 

• Wireless-Enabled Smart Meter: Smart meter locations can be located on 

outside walls or in electronic vaults in below grade locations. Size limitation 

would limit the antenna size and gain.
81 

o Antenna Gain: Requires an omni-directional antenna, gain typically -

1.0 dBi to +1.0 dBi

o Antenna heights will typically be lower and locations can be indoor, below 

grade, or housed in a cabinet

o Lower Transmit Power: Most locations will require limitations to meet 

human safety exposure limitations

• Mobile Handheld Device: This may not necessarily be a common application for 

Smart Grid since it can in most cases be covered with the use of public networks. 

Nevertheless for completeness it is worth including. Mobile handheld devices 

have limited antenna size and lower transmit power. The transmit power is 

constrained by the battery capability. For this usage model the link budget and 

path loss model must account for:

o Lower Antenna Gain: Must be omni-directional, typically -1.0 dBi to 

0 dBi

o Antenna Height: Typically 1.5 meters

o Lower Transmit Power: Typically 200 mW or less

o Building / Vehicle Penetration Loss: To support indoor or in-vehicle 

operation

o Loss due to absorption by the person holding the device

Taking all the above factors into account can result in significant differences in the 

system gain and link budget for various types of Smart Grid use cases. A fixed outdoor 

terminal for backhauling a DAP compared to a mobile handheld device or wirelessenabled smart meter can result in 30 dB or more difference in link budget and up to 

50 dB for indoor basement-located smart meters. 

5.2.2.1.1 Fade Margins 

Fading in a propagation path is usually characterized as shadow fading which is slow or 

medium-term and fast fading. Shadow fading tends to be the dominant fading 

mechanism and is primarily due to obstructions in the propagation path. Shadow fading 

generally follows a Log-normal distribution and fast fading, which is primarily due to 

multipath, is Rician distributed when a dominant signal is present, as is the case for an 

LoS or near-LoS path, and is Rayleigh distributed when there is no dominant signal 

present. In the latter case it is simply the sum of Gaussian variables. Multipath fading 

has also been shown to follow a Nakagami distribution which is defined as the sum of 

multiple independent Rayleigh distributed signals. In any case fast fading due to 

scattering and multipath is generally not as significant as the deep fades caused by 

shadowing. Figure 24 shows a comparison of Log-normal shadow fading and multipath 

fast fading. 
82 

Figure 24 - Comparison of shadow fading and fast fading 

In the link budget it is important to allow for sufficient fade margin to ensure sufficient 

link availability for terminals or actors at the cell edge. Since shadow fading, the 

dominant fading mechanism, is governed by a Log-normal distribution, it is a 

straightforward calculation to determine the probability that the signal level at the cell 

edge will be sufficient to maintain a specific level of performance. Figure 25 shows the 

relationship between fade margin, standard deviation, and cell edge availability. 
83 

Figure 25 - Cell edge availability compared to fade margin and standard deviation 

Typical fade margins for non-LoS propagation analysis are provided in Table 12. These 

values are expected to result in an availability of at least 90 % at the cell edge. It is 

important to emphasize that this does not necessarily mean there is 10 % likelihood that a 

complete outage will occur at the cell edge. Most well-planned deployments will specify 

a cell edge performance requirement that will be several dB above the absolute threshold 

required to maintain the link. If, for example, cell edge performance is based on 

operation with QPSK and 1⁄2 rate-coding, support for HARQ with 6 repetitions will 

provide approximately 8 dB of additional margin before a complete outage occurs. The 

availability with respect to a complete outage at the cell edge is therefore approximately 

99 %. 

Table 12: Typical fade margins 

Indoor 

(dB) 

Urban 

Outdoor 

(dB) 

Urban 

Outdoor to 

Indoor 

(dB) 

Suburban, Rural, 

Types A, B, C 

Outdoor 

(dB) 

Standard 

Deviation (σ) 

4 to 8 6 7 8

Shadow Fade 

Margin (Fs) 

5.2 to 10.3 7.8 9.1 10.3

Fast Fade 

Margin 

2 2 2 2

Total Fade 

Margin (Fm) 

7.2 to 12.3 9.8 11.1 12.3

For deployments in which higher availability is required or alternatively where lower 

availability may be acceptable, the curve in Figure 26 provides a simple relationship 

between cell edge availability and Fs/σ. 
84 

Figure 26 - Cell edge availability versus Fs/σ

5.2.2.1.2 Interference Margin 

Both self-interference and interoperator interference must be considered. If one has 

dedicated access to a block of spectrum interoperator interference will generally not be an 

issue but the effects of self-interference or co-channel interference (CCI) must be taken 

into account. Using a 3-sector cell as an example, Figure 27 shows two frequency resuse 

schemes that can be employed. Reuse 1 requires less total spectrum for a given channel 

bandwdith but one must allow for sector to sector CCI and cell to cell CCI. Reuse 3 

requires three times more spectrum but sector to sector CCI is replaced with adjacent 

channel interference which is considerably less. Cell to cell interference is greatly 

reduced as well. Typical values for interference margin (Im) are: 

• Reuse 1: Im = 2.0 dB to 4.0 dB 

• Reuse 3: Im = 0.5 dB to 1.0 dB 

Figure 27 - Frequency reuse with 3-sector BS 

Inter-operator interference can be a significant factor when the same block of spectrum is 

shared with other operators and applications. This situation will arise with operation in 

the unlicensed bands, sharing with municipalities in the US public safety bands, or when 

using the 3650 MHz to 3700 MHz lightly-licensed band. Typically, due to the higher 

incidence of network traffic, interference will be worse in higher density urban areas as 

opposed to what would be experienced in rural environments. 

Chan 

A

Chan 

C

Chan 

B

Reuse 3

• Each sector has 

dedicated channel

• Requires more spectrum

Chan 

A

Chan 

A

Chan 

A

Reuse 1

• One channel reused in 

each sector

• Must manage inter-sector 

interference
85 

Some recommended margins for inter-operator interference are shown in Table 13. 

Table 13: Inter-operator interference margins 

Urban Suburban Rural 

Inter-Operator Interference 6 dB to 8 dB 4 dB to 6 dB 2 dB to 4 dB

5.2.2.1.3 Penetration Loss 

For terminals or SSs located in indoor environments, it is necessary to account for the 

resulting signal loss as it passes through the medium separating the outdoor BS and the 

indoor terminal. When an RF signal hits an object, such as a wall, with dimensions larger 

than a wavelength a portion of the signal will be reflected and remainder will pass 

through the object with some additional loss before emerging on the other side. Arriving 

at a reasonably accurate estimate for the net penetration loss must take into consideration 

a range of factors including: 

• Operating frequency 

• Angle of incidence 

• Wall or barrier material 

• Wall or barrier thickness and surface texture 

• Number of walls signal must pass through 

• Existence and number of windows or openings in the wall 

Many field tests have been conducted over the years at various frequencies. Some of 

these studies have investigated the impact of specific materials, such as plywood versus 

cinderblock walls while other studies have been conducted with various buildings with 

only a brief description of wall materials and other field studies included very limited or 

no information regarding the building type or wall materials. Most of these studies have 

been done at specific frequencies and in most cases there is a significant spread in the 

data. This makes it challenging to provide penetration loss predictions for the full 

frequency range of interest covering the many outdoor-to-indoor scenarios likely to be 

encountered in a Smart Grid network. Nevertheless it is of value to make this attempt 

with the limited data that is available. In Table 14, penetration loss data has been taken 

from various sources [29], [30], [31], [32] and shown in bold italics under the headings 

closely consistent with what was reported20. Other values have been inserted to fill out 

the table. The last row in Table 14 provides a suggested margin that should be included 

in the link budget to account for the spread in actual penetration loss that can be expected 

over a range of building types in a typical geographical area. 

 

20 It is important to mention that the WINNER II and ITU-R M.2135-1 indoor models suggested little or no

difference in wall or floor penetration loss for measured data at 2 GHz and 5 GHz. 
86 

Table 14: Penetration loss (dB) by frequency and location 

Frequency 

(MHz) 

Inside 

Vehicle 

Indoor 

Residential 

Indoor 

Business, 

Industrial 

Indoor 

Basement 

Indoor 

Meter Vault 

700 9.0 7.5 10 17 27

1000 9.0 7.7 13 18 28 

2000 9.0 11.6 20 24 30

3000 9.0 13 24 28 32

4000 9.0 14 27 30 34

5000 9.0 15 29 31 35

6000 9.0 16.2 31 31.5 36

Suggested 

Margin (σ) 5 5 6 6 8 

5.2.2.2 Deployment Trade-offs 

In this section we look at some of the trade-offs that must be considered in any wireless 

deployment. Whether deploying in a high density urban area or a low density rural area 

achieving ubiquitous coverage with the minimum number of BSs is always a key 

planning goal. In addition to coverage, urban areas will also have capacity requirements 

to consider. In the following subsections we will look specifically at trade-offs with 

respect to range and coverage based on path loss predictions. While path loss is a 

primary factor for range predictions it is important to mention that it is not the only 

factor. A number of equipment and technology-related factors can also play a large role 

since, in many cases, wireless equipment designs are tailored to specific deployment 

scenarios. Path loss, however, is a factor common to all land-based wireless systems so it 

is the key metric used in the following analysis. 

5.2.2.2.1 BS and SS Antenna Heights 

All of the large scale propagation models have a term that describes the path loss 

dependency on the path length, d, for d well beyond the near-field distance from the 

transmit antenna. This distance-dependent term, which is applicable in either the DL or 

UL direction, can be expressed as: 

Total Path Loss (PL) vs Distance = 10nlog10(d) 

where n, is generally referred to as the path loss exponent, is equal to 2 for free space, 

and is greater than 2 for obstructed or non-LoS paths. 

The path loss exponent is dependent on parameters derived from the measurement data 

and, in most cases, has a direct dependence on BS antenna height. The value of n is 

plotted in Figure 28 for the different outdoor large scale models discussed in the previous 

sections. It is important to observe the rapidly increasing magnitude of n for BS antenna 

heights lower than 30 m as predicted by the Modified Erceg-SUI path loss model. It is 

also of interest to note that, other than the WINNER II rural case the other path loss 

models exhibit the same distance dependency for the different deployment regions. Since 

these models specify that BS antenna heights must be above average surrounding roof 

tops, this is a realistic expectation. 
87 

Figure 28 - Path loss exponent relative to BS antenna height 

The potential impact of the additional path loss due to lower BS antenna heights is best 

analyzed by looking at the impact on cell range and coverage. In Figure 29 the relative 

range predicted by the Modified Erceg-SUI model for BS antenna heights from 7 m to 

80 m is plotted. 

Figure 29 - BS antenna height impact on range prediction 

The effective coverage area varies as the square of the range prediction. Being restricted 

to a BS antenna height of 7 m to 10 m can result in a need for 10 to 20 times as many BSs 

compared to having a 70 m to 80 m structure available for the BS to cover a specified 

geographical area. Since the relative BS antenna height has such a significant impact on 

range and coverage area it is worth looking at the alternative approaches in more detail to 

provide some insights to help evaluate the trade-offs based on several criteria including a 

0 %

20 %

40 %

60 %

80 %

100 %

0 10 20 30 40 50 60 70 80 90

Relative Range

BS Antenna Height in meters

Relative Range vs. BS Antenna Height

Type A

Type B

Type C
88 

qualitative cost comparison. In Table 15 we look at four possibilities for the BS 

deployment: 

A) Use an existing utility pole 

B) Use an available neighboring structure 

C) Lease existing tower space 

D) Build dedicated standalone tower 
89 

Table 15: Trade-offs for BS deployments 

A) Use an Existing Utility 

Pole 

B) Use an Available 

Neighboring Structure 

C) Lease Existing Tower 

Space 

Availability & Location 

Large number of existing poles to select from for optimal coverage e.g., in utility easements or rights-of-way, as restricted by: 

• Available space on the pole

• Utility pole usage standards

Lower availability as compared to utility poles 

• Further restrictions for: a) 

structure suitability for equipment attachment, b) property (non-utility) access privileges, c) electrical power agreements with structure owner (non-utility), d) required permitting 

Considerable lease tower options exist but existing towers may not: 

• be optimally located for SG 

purposes 

• have space available for 

additional antennas 

Locate by: 

• R

wo

• G

ju

• A

a

Time to Deploy 

Relatively short deployment times for existing poles 

• Slightly longer deployment 

durations for new or replacement poles 

• Some localities require 

additional permitting for higher than routine pole heights 

More time than utility poles due to: 

• Time to find and negotiate 

terms with property owner other than the utility 

• To gain necessary permit(s) 

approval 

• Performing structural 

analysis as required 

Considerable time investment, (but normally less than option D), to: 

• Negotiate terms with tower 

owner 

• Structural analysis for 

leased tower space 

• Gain permit(s) approvals 

e.g., National Environmental Policy Act (NEPA) requirements 

Consid

• F

• C

• N

p

• S• G

e

• D

eis

• B
90 

A) Use an Existing Utility 

Pole 

B) Use an Available 

Neighboring Structure 

C) Lease Existing Tower 

Space 

BS Height Limitations 

• Typically 7 m to 15 m 

antenna heights for utility distribution poles. 

• Poles heights up to 30 m are 

also commonly used, for special utility electric grid or telecomm purposes 

• Permits frequently required 

for the higher pole heights especially in urban areas 

• Multi-story buildings, power 

plant structures are generally available for higher antenna heights than option A 

• Unless the antenna heights 

are measurably higher than option A, the range impact may only be marginally better 

• Higher antenna heights may 

increase the observed RF 

noise floor in some areas and spectrum bands 

• Height restricted to space 

available on tower 

• Restricted by capacity of 

tower to carry the additional antenna, mounting gear, cables, and antenna wind loading considerations 

• Higher antenna heights may 

increase the observed RF 

noise floor in some areas and spectrum bands 

• Ca

and res60 

• Hi

incnospe

• FA

reqfor 
91 

A) Use an Existing Utility 

Pole 

B) Use an Available 

Neighboring Structure 

C) Lease Existing Tower 

Space 

Relative Costs 

• Least expensive for both 

Capital Expense and Operating Expense21 on a per 

BS basis, but requires greater number of BSs to provide coverage as compared to the other options 

• More backhaul points, but 

with potentially lower backhaul costs per BS due to reduced capacity needs than for the other options 

• Generally greater Capital 

Expense than option A as driven by type and height of facility and the additional BSsupport structure requirements and facility structural analysis, and any addition permitting 

• Generally greater Operating 

Expense than option A as driven by property owner, lease, or rental fees 

• Generally fewer BSs needed 

to provide the same coverage as option A 

• Lower Capital Expense for 

required equipment than option D, as offset by Operating Expense for tower space lease based on height placement on the tower. 

• Capital Expense requires 

tower loading / structural analysis. 

• Requires fewer BSs needed 

to provide coverage than option A and some option B locations-heights. 

• Hi

OptowBSA. 

• To

to spa

• Ma

incprobuele

• Fe

the A 

21 For Table 15 Note: Capital Expense (or Expenditure) and Operating Expense (or Expenditure) 
92 

The impact of SS / terminal antenna height on range is shown in Figure 30. In the path 

loss models, for the valid range of antenna heights, this factor is accounted for as a fixed 

quantity independent of path length and independent of frequency. From a deployment 

standpoint, especially in a FAN, there is some control on the BS antenna heights but 

limited control on terminal antenna heights. Meter locations, for example, are already in 

place and must be dealt with wherever they are located. 

Figure 30 - Terminal antenna height impact on range

5.2.2.2.2 Impact of Spectrum Choices 

When spectrum choices exist for the deployment of a wireless network it is important to 

quantifiably assess the trade-offs. Based on the previous discussions on path loss models 

it is clear that spectrum choice will be a key factor in determining cell range and 

coverage. Figure 31 shows the predicted range relative to 2000 MHz assuming the same 

link budget over the total frequency range from 700 MHz to 6000 MHz with a BS 

antenna height of 7 m and 30 m for terrain Type A. This analysis predicts almost a 4 to 1 

difference in range which results in approximately 15 times difference in coverage area 

for a 700 MHz deployment versus a deployment at 6000 MHz. For an LoS PtP case, 

assuming sufficient clearance for the first Fresnel zone, greater than 8 to 1 range 

difference is predicted. 
93 

Figure 31 - Range dependency on frequency 

To completely assess the frequency trade-offs other factors must also be considered. The 

above analysis assumes the same link budget for frequencies between 700 MHz and 

6000 MHz. It is important to point out that some of the advanced antenna techniques that 

are currently available for wireless deployments may not be practical in the lower bands. 

This has the effect of narrowing the range gap. 

Higher order MIMO systems for transmit and receive diversity are becoming more and 

more prevalent. For best results these techniques require a high degree of de-correlation 

between the antennas. For second order MIMO systems dual polarization can be used 

effectively in any of the frequency bands being considered without having to provide a 

large separation between the antennas to ensure the signals are uncorrelated [33]. For 

higher order MIMO antenna systems, however, the antenna separation would have to be 

on the order of 3 wavelengths to 5 wavelengths to maintain sufficient de-correlation 

between the antennas for good receive or transmit diversity performance. Since the 

wavelength at 700 MHz is almost 0.5 m, these antenna systems would not be practical in 

these lower frequency bands. 

Beamforming is another approach that can be considered to improve the system gain in 

the higher frequency bands but would be impractical in the lower bands due to the size. 

These systems call for arrays of 4 antennas to 8 antennas spaced 0.5 wavelengths apart. 

A 4-antenna array in the 700 MHz band would be in the order of 3 m to 5 m in width. 

Taking these factors into consideration plus higher antenna gains can result in a 6 dB to 

8 dB higher link budget at 6000 MHz compared to 700 MHz thus reducing the range 

difference to less than 3:1. This is still a significant difference however, in that it requires 

almost 10 times as many BSs at 6000 MHz for ubiquitous non-LoS coverage for a given 

geographical area as compared to the BS requirements for a 700 MHz deployment. 

To achieve true LoS with point-to-point links antenna heights must be selected to provide 

adequate Fresnal zone clearance as was discussed earlier. A good guideline is 60 % 
94 

clearance but in general one would like to plan for full clearance anticipating that 

propagation path changes occurring over time might eventually infringe on the first 

Fresnel zone. This requirement can also be somewhat more challenging in the lower 

frequency bands. If one end of a 700 MHz link is set at an antenna height of 10 m, as 

shown in Figure 32, the other end of the link would have to be above 32 m to provide 

first Fresnel zone clearance for a 3 km path length. On the other hand, any frequency 

above 2250 MHz would ensure clearance with antenna heights of 10 m. Alternatively, if 

the antenna heights at each end of the link were limited to 10 m, a 700 MHz link would 

be limited to a path length of less than 1 km to ensure first Fresnel zone clearance. 

 

Figure 32 - Comparing 700 MHz and 2250 MHz for 1st Fresnel zone clearance 

5.2.3 Estimating Channel and BS Sector Capacity 

In the determination of the range capability for a specific wireless technology it is 

necessary to specify a threshold SNR to meet an acceptable throughput performance and 

link availability for SSs or actors located at the cell edge. 

Many of the SSs or actors located randomly throughout the coverage area will experience 

significantly higher SNRs and thus be capable of higher throughput performance and 

higher availability. 

Assuming a uniform distribution of SSs, the SNR relative to the cell edge performance 

can be determined based on the specific path loss model used and the BS antenna height. 

The plot in Figure 33 relates the percentage of coverage area for the SNR compared to 

the cell edge for different values of the path loss exponent. 

0

5

10

15

20

25

30

35

0.0 0.5 1.0 1.5 2.0 2.5 3.0

F1 in meters

Path Length in kilometers

First Fresnel Zone 

Terminal Height 10 m, 3 km Path

700 MHz

2250 MHz

LOS
95 

Figure 33 – Signal to noise ratio (SNR) and cell coverage area 

The higher SNR that prevails over a large percentage of the coverage area results in a 

higher link availability, as well as enabling a percentage of subscriber terminals to 

operate at higher modulation efficiency. 

As described earlier, the cell edge link availability is determined by the fade margin and 

can be predicted by assuming shadow fading is a log-normally distributed random 

variable. Figure 34 shows the relationship between the availability at the cell edge, in 

this case 90 %, and the predicted availability over the remainder of the coverage area. 

Note that this applies to a single cell or BS and a terminal located at the cell edge whose 

connection is restricted to that BS. For a typical multi-cellular deployment, terminals or 

actors at the cell edge with omni-directional antennas will generally have access to more 

than one additional BS. This scenario results in a significantly higher availability due to 

the very low probability that deep fades will occur simultaneously on multiple 

propagation paths. 
96 

Figure 34 - Link availability relative to path length 

Alternatively it may be of interest to look at the probability of an outage (see Figure 35), 

where in this case an outage is defined as not meeting a specified data rate. Whereas the 

probability of an outage is 10 % at the maximum range, it is considerably lower at a 

reduced path length. 

Figure 35 - Outage probability relative to range 

The effective spectral efficiency also increases for actors or users closer to the BS, which 

translates directly to increased data throughput for those users. This is illustrated in 

Figure 36, which shows the relationship between signal to noise ratio (SNR) per symbol 

(Es/No) and the Symbol Error Rate (SER). Note that the graph, for illustrative purposes, 

assumes no Forward Error Correction (FEC). 
97 

An increase in SNR of approximately 7.5 dB for a given SER will result in an increase of 

the modulation efficiency from QPSK to 16QAM, a 2:1 improvement. A further SNR 

increase of about 6 dB to 64QAM provides an additional 50 % increase in spectral 

efficiency while maintaining the same SER. 

Figure 36 - Symbol error rate (SER) and Es over No

The addition of FEC can provide significant improvement in the SER or alternatively 

reduce the required threshold SNR for satisfactory performance. With respect to Figure 

36 the addition of FEC would, in effect, move the plots to the left by an amount dictated 

by the type and amount of FEC. FEC, of course, adds redundant bits to the transmitted 

signal resulting in a lower effective data rate for the same overall channel bit rate. 

Table 16 provides a view of what may typically be supported with any specific wireless 

technology with a single transmit and single receive antenna in either the DL or UL 

direction. Many of today’s wireless technologies take advantage of advanced antenna 

systems including MIMO (Multiple Input Multiple Output). The use of multiple 

antennas with spatial multiplexing can increase both the DL and UL spectral efficiency. 

Although the following analysis assumes Single Input Single Output (SISO) the basic 

concept is applicable with multiple antenna systems as well. 

An SNR for a specified SER or BER would be associated with each modulation 

efficiency and code rate. With the most robust modulation efficiency and several ARQ 

or HARQ repetitions a satisfactory error rate may be achieved with an SNR of less than 

zero whereas 64QAM with 5/6 rate-coding would require an SNR of 20 dB or more. 

Table 16: Modulation and spectral efficiency 

Modulation Code Rate Repetitions Spectral Efficiency 

((b/s)/Hz) 

QPSK 1/2 6 0.166 

QPSK 1/2 4 0.25

QPSK 1/2 2 0.5
98 

Modulation Code Rate Repetitions Spectral Efficiency 

((b/s)/Hz) 

QPSK 1/2 0 1.0

QPSK 3/4 n/a 1.5 

16QAM 1/2 n/a 2.0 

16QAM 3/4 n/a 3.0 

64QAM 1/2 n/a 3.0 

64QAM 2/3 n/a 4.0 

64QAM 3/4 n/a 4.5 

64QAM 5/6 n/a 5.0 

Using a table similar to Table 16, along with the applicable SNR for each modulation and 

code rate, one can determine the channel spectral efficiency net of FEC relative to the 

range. This is shown in Figure 37 for different path loss exponents. The probability that 

the received signal level will be sufficient to support the SNR required for each 

modulation and code rate at different path lengths will be the same as that used to predict 

the maximum range. Since Figure 37 relates spectral efficiency to the relative path 

length the curves for different values of n start at the same point, namely the minimum 

spectral efficiency used to define the threshold SNR. With reduced distance the spectral 

efficiency increases to the value predicted for 64QAM with 5/6 rate-coding. The rate at 

which the spectral efficiency increases to its maximum value is a function of the path loss 

exponent, n. 

Figure 37 - Spectral efficiency (SE) increases with decreased range 

In practice, whether it is due to the path loss model selected for the range analysis or the 

conditions under which the model is being applied, the higher path loss exponent will 

generally result in a lower range prediction but, as the curve shows, a greater percentage 

of the predicted cell coverage area will experience a higher channel spectral efficiency. 

This is shown more clearly in Figure 38 where the spectral efficiency is plotted versus the 

relative coverage area. 

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 0.2 0.4 0.6 0.8 1.0

Spectral Efficiency (b/s)/Hz

Distance to BS relative to Maximum Range (d/D)

Spectral Efficiency vs d/D

n= 6

n= 4

n= 3
99 

Figure 38 - Spectral efficiency (SE) relative to cell coverage area 

Assuming the terminal devices are uniformly distributed over the cell coverage area, the 

average channel spectral efficiency can be found by estimating the area under the curve. 

This can be done by breaking the area into m segments and calculating the average of the 

spectral efficiencies over all of the segments. 

Wnopq =

1

r

# pqN

s

Nt2

As an example for the path loss exponent, n= 6: 

AvgSE = 2.0 (b/s)/Hz 

Although average channel spectral efficiency is an important metric, of greater interest is 

the average channel capacity, and most importantly actual data throughput at the 

application layer or goodput. Multiplying the average spectral efficiency (AvgSE), as 

shown above, with the channel BW provides the average channel throughput. This 

however, only takes overhead due to FEC into account. For the net channel goodput, a 

number of additional channel overhead factors must also be taken into account. These 

include: 

• Additional PHY overhead to account for control or pilot channels or sub-channels 

• Layer 2 (MAC / Data Link) overhead 

• Layer 3 to Application Layer overhead for additional protocols, headers, etc. 

• Encryption overhead 

Denoting this additional overhead as ChOH, the average channel goodput is easily 

calculated. 
100 

Avg Channel Goodput = AvgSE × ChanBW × (1 – ChOH) 

It is useful to define the term net spectral efficiency (NetSE) given by: 

NetSE = AvgSE × (1-ChOH) 

It should be noted that overhead (OH) may be accounted for differently in how it is 

allocated between layer 1 and layer 2 with different technologies. What is important is 

that all of the OH factors must be taken into account. 

In doing this analysis it is also important to consider the channel overhead (ChOH) in 

both the UL and DL channels as these may not always be the same. For most Smart Grid 

use cases the UL traffic will be greater than the DL traffic22 so the UL channel data 

capacity or UL goodput will be the metric of interest for assessing BS capacity 

requirements. 

If a higher data goodput is required to meet the data demand in high population density 

environments, BSs can be deployed with a closer spacing. When the range that each BS 

must cover is less than its maximum range capability the channel capacity is increased 

due to the higher average SNR over the entire coverage area. In the above example 

limiting the range to 0.7D results in an AvgSE = 3.05 (b/s)/Hz, a 70 % increase in 

throughput. 

5.2.4 Physical Layer (PHY) Model 

The purpose of the PHY model is to estimate the probability that a transmission attempt 

fails due to channel errors caused by noise or interference. The transmission failure 

probability takes into account factors affecting the link budget, including transmission 

power, antenna gains, channel attenuation, thermal noise, background interference, the 

number of contending stations (if the channel is shared), and the spread spectrum 

processing gain, if applicable. Depending on the level of modeling, the PHY model may 

also explicitly model the stages of the transceiver, such as channel equalization, 

demodulation, and forward error correction, resulting in a bit error rate, symbol error rate, 

or block error rate. Alternatively, the PHY model may abstract some of these functions 

and model them with an overall required Es/N0 or Eb/N0 (ratio of energy per bit to noise 

power spectral density23), wherein the probability of transmission failure is reflected as 

the probability that the received SINR per bit exceeds the required Eb/N0. As part of the 

modeling framework, the PHY model provides the MAC sublayer model with a 

conditional probability of transmission failure. For example, with a contention based 

MAC, the MAC model supplies the PHY model with the number of contending 

transmissions. Given the parameters of the link budget and channel statistics, the PHY 

model then returns the probability that the transmission of interest is unsuccessful 

conditioned on the number of contending transmissions. 

 

22 The exception would be when firmware is being updated. During these instances DL traffic can be more

dominant. 

23 The energy per symbol to noise density can be found by the expression, Es

/No = log2(M) × Eb/No , where 

M is the modulation index. Both Es/No and Eb/No are directly related to SNR. 
101 

5.2.5 MAC Sublayer Model 

The MAC sublayer model can be either analytical or simulation-based. The relative 

complexity is determined by the preferences and needs of the user. The MAC sublayer 

model receives inputs based on the application requirements and the wireless (or wired) 

technology that is being used to transport the data; the model interacts with both the PHY 

model and the coverage model. The MAC sublayer model is responsible for returning 

values for the following performance metrics for the communications system: 

• Reliability 

• Mean packet delay (latency) 

• Layer 2 Throughput 

• Encryption 

Reliability is defined as the probability that a packet originating from a sending node’s 

MAC sublayer is correctly received by the corresponding MAC sublayer at the receiving 

node. Thus the reliability is defined with respect to a single link, rather than on an endto-end or edge-to-edge basis. For MAC sublayers with a shared channel, where there is 

contention for resources, the reliability is the probability that the packet does not collide 

with any packets that are transmitted by other senders and that the packet is not corrupted 

by channel errors. If the channel is dedicated to the sender (no contention), then the 

reliability is simply the probability that the packet does not experience any channel 

errors. The mean packet delay is the average time from the passage of the packet to the 

sender’s MAC sublayer from the protocol layer immediately above to the delivery of the 

packet by the receiver’s MAC sublayer to the protocol layer immediately above it. The 

mean packet delay includes the following: 

• The time the packet spends in the sender’s MAC sublayer’s transmission buffer 

• The processing time at the sender’s MAC sublayer 

• The time required to transmit the packet, which is the packet length in bits divided 

by the PHY channel data rate in bits per second 

• The time spent waiting to retransmit the packet if it encounters collisions (in the 

case of a contention-based MAC protocol) or channel errors 

• The propagation delay between the sender and the receiver 

• The processing time at the receiver’s MAC sublayer 

• BS to BS handover delay (applicable for mobile terminals) 

The throughput is a measure of how efficiently the channel is being used, and it is 

measured in units of application bits per second. The model computes two types of 

throughput. 

• The first type is the average throughput, which is the product of the offered load 

at the application layer and the packet reliability. Note that this implies that the 

ratio of the throughput to the offered load is always a number between 0 and 1. 

• The second type of throughput measured by the model is the instantaneous 

throughput, which is the ratio of the mean number of application data bits per 

packet to the mean packet delay. This gives the effective channel rate 
102 

experienced by a packet that is ultimately successfully sent across the link, even if 

it requires retransmissions. 

The major external inputs that do not depend on the particular MAC technology are the 

number of devices accessing the channel, the mean packet generation rate of each device, 

and the mean packet size. The mean packet generation rate is typically given in units of 

packets per second; the actual packet generation process is arbitrary. Packets can arrive 

according to a deterministic process, in which case the mean generation rate is simply the 

actual generation rate, or they can arrive according to a random process (e.g., a Poisson 

arrival process). The size of the packet typically includes the size of the application data, 

as well as the combined size of all headers, including the MAC sublayer and PHY 

headers. The packet size can be deterministic or random, depending on the applications 

that are being modeled. There are additional inputs that are unique to the MAC 

technology that is being modeled. In the case of a contention-based MAC technology, 

these parameters can include the number of times the MAC sublayer will attempt to 

transmit a packet before giving up and dropping it, rules for handling packet collisions, 

such as the amount of time that the MAC sublayer must wait to retransmit a packet after 

it has collided with a packet from another transmitter, and the amount of time the sending 

MAC sublayer must wait for an acknowledgement of a transmitted packet before taking 

further action. 

Non-contention MAC technologies will use different parameter sets. The PHY model 

exports the probability of transmission failure, Pfail, to the MAC sublayer model, which 

uses it to help compute the output metrics. For instance, if modeling a very simple MAC 

sublayer that uses dedicated resources (so no contention) and no retransmissions, it would 

be found that the reliability is equal to (1 − Pfail), and the mean delay of successfully 

received packets is the sum of the propagation delay and the transmission time. The 

coverage model exports the maximum Tx-Rx distance to the MAC sublayer model. With 

only a user population density, the maximum Tx-Rx distance can be used to compute the 

coverage area and size of the covered user population. 

5.2.6 Multi-Hop (or Multi-Link)24 Model 

When the PHY parameters of a wireless link are such that the link is coverage limited, 

the effective coverage can be extended by routing through a sequence of multiple 

connections or links, denoted as a multi-hop, rather than through a single link alone. The 

MAC model generates performance metrics for single links; the multi-hop model, on the 

other hand, works interactively with the MAC model to generate end-to-end performance 

metrics for multiple hops or relays. As illustrated in Figure 9, the multi-hop model 

accepts single-link performance metrics as input from the MAC model. Subsequently, 

the multi-hop model generates the same classes of performance metrics for multiple hops. 

The actual sequence of links depends on the pair of source and destination nodes and the 

pair-wise link metric between the intermediate nodes. Common link metrics are 

minimum-hop and minimum-airtime. The resultant routing topology indicates the routes 

through which traffic is forwarded through the multiple hops. The routing topology 

 

24 Multi-hop is used to describe inter-technology BS to BS connections and multi-link to describe intratechnology BS to BS connections 
103 

affects links in a different manner. For example, if a link is forwarding traffic from 

multiple sources, it will have a heavier traffic load than otherwise. In particular, if the 

destination of all source nodes is a single BS or DAP, the backhaul links connected 

directly to the destination or DAP will be forwarding traffic from all other sources. This 

translates to a higher offered load for those links. The offered load of the source is an 

input to the MAC model from the application requirements. The MAC model also 

accepts the routing topology as input from the multi-hop model and in turn computes the 

offered load of all links accordingly. Source node facing links from the DAP to the other 

source nodes in this multi-hop network will similarly have higher traffic density 

dependent on the number of additional sources served the DAP. 

5.2.7 Modeling Latency 

When considering a Smart Grid application consisting of two-way transactions between 

two actors, one must consider the amount of time that can be tolerated for completing the 

two-way exchange of data between the actors involved in the transaction [5]. This is 

typically referred to as the maximum latency for this transaction. Multiple factors affect 

the total delay or latency of a transaction that usually includes: processing time in system 

servers, delays in database access, and communications or network delays to mention a 

few. Of these, the communication or network delay is considered and analyzed in further 

detail in this section. It must be noted that the network delay will consume only a 

fraction of the total maximum transaction latency. The remaining fractional portions of 

the total transaction latency will be allocated to other system components. 

The analysis of the network delay is usually addressed by analyzing each link, hop, or 

segment of the network system forming the network path between two actors, A and B. 

The total network delay is then the sum of the individual delays contributed by each link, 

hop, or segment. The total two-way network delay would then include the network delay 

encountered in transmitting a transaction data payload from actor A to actor B plus the 

network delay encountered in transmitting the transaction’s response data payload from 

actor B back to actor A. 

As indicated above, a total network latency analysis must consider the discrete delays 

encountered in each segment or link through which transaction data payloads traverse. 

Therefore, even the fraction of the total transaction latency allocated for the network 

delay must be further sub-divided and allocated to the multiple segments in the network. 

In some of these network segments the actual delays may be insignificant while in others, 

the delays may be larger and must be analyzed more completely for a more accurate 

analysis. In general the delay encountered in a segment is related to the channel 

bandwidth and goodput (defined in section 5.2.3), the size of the data packet to be 

transmitted, and the congestion encountered at that network segment. If a network 

segment is idle when a data payload arrives, it is usually transmitted immediately and the 

only delay is the delay encountered in preparing the data for transmission plus the delay 

in actually transmitting the data payload at the goodput rate for the network segment of 

interest. For greater precision the propagation delay over the length of the segment path 

must also be included. However, for short path lengths, this delay is considered 

insignificant and, in most cases, ignored. If, on the other hand, a network segment is 

receiving multiple data payloads for transmission in a short period of time, there is a 
104 

finite probability that some data payloads will encounter congestion from other data 

payloads waiting for access to the network segment or channel. When payloads compete 

for access to a congested channel queuing comes into play and results in additional delay. 

Queuing theory, which is deeply rooted in probability theory, has been the subject of 

extensive research over the years for a wide range of applications that has resulted in 

many different mathematical models for predicting queuing-induced delay. The goal of 

this section is to provide an introduction to this subject and provide some insights as to 

how the channel goodput, message or payload size, and message or payload rate relate to 

the delay caused by queuing in a congested channel in a single network segment. 

The first approach uses a binominal distribution technique to establish an estimation of 

the probability of meeting a specific latency value. The second approach uses a more 

traditional response time analysis technique based on M/D/1 25 and M/M/1 26

mathematical queuing models to derive the average response time for transactions 

transmitted over a network segment. Each model is intended to show the effects on 

latency when increasing the number of nodes or actors competing for access to a network 

segment of a given capacity or goodput. 

5.2.7.1 Binomial Distribution Model 

In this section we describe an approach based on a binomial distribution for determining 

the number of end-terminals or actors that can be supported by a specific channel while 

meeting a specific latency requirement. When a number of packets are competing for 

access to a limited resource, there is a probability at any instance of time that a specific 

packet carrying a message or data payload will or will not gain immediate access to the 

channel for transmission over the link. As indicated earlier, it must be noted that an 

individual network segment is not the only contributor to total network latency (or delay) 

but when a channel is operating at or near its capacity, it will often be the dominant 

contributor and thus an important one to model. The key parameters required for 

modeling this contribution to latency are: 

• The average channel goodput (CGP) 

• The average transmitted packet size (PAVG) (note that large data payloads or 

messages may be segmented into smaller packets for transmission) 

• The rate at which messages or packets are being transmitted (RMSG) or 

alternatively, the average time between messages or packets (TMSG) 

• The probability a packet is transmitted or received within a specified time window 

(PMSG) 

The average channel goodput = CGP = Net Spectral Efficiency multiplied by the Channel 

BW where the Net Spectral Efficiency is defined as the average spectral efficiency at the 

 

25 The notation follows Kendall’s notation where M stands for Markov (arrival times), D stands for 

deterministic (service times), and 1 stands for number of servers or in this case, transmission links.

26 Another related model is the M/M/1 Queuing model where the service times are not constant but are also 

described by a Poisson process 
105 

application layer as described in section 5.2.3. This takes into account all of the channel 

overhead factors including the higher layer protocols, headers, and encoding overhead. 

For any given actor in a Smart Grid network there can be hundreds of messages that must 

be transmitted within any 24 hour period. The message rates for different types of 

information can range from several messages per hour to one message per day, and the 

size of the message payload can vary from 25 bytes to several thousand bytes. From the 

detailed SG Network System Requirements Specification, the average message rate 

(RMSG) per actor can be determined and the average message payload or packet size 

(PAVG) can be calculated. The average time between messages is then given by: 

TMSG = 1/RMSG ; 

for TMSG in seconds RMSG must be expressed in messages per second. 

The time in seconds it takes for the average packet to be carried over the channel is given 

by: 

TPKT = 8 × PAVG / CGP ; 

where CGP is the channel goodput in b/s 

By using a binomial distribution analysis mythology, we require two additional 

parameters: the number of trials and the packet probability. One way to do this is to 

assume that the number of trials is equal to the number of time slots that occur within a 

specified latency period. This value is L/TPKT (rounded down to the nearest integer). 

Note that L must be greater than TPKT. The probability that a message event falls within 

the time window defined by L is: PMSG = L/TMSG. For the model, L is chosen to be the 

fraction of the overall latency at the application layer in seconds which has been 

apportioned to this network segment. Using these assumptions, the cumulative binomial 

distribution function is used to analyze the congestion that may occur during a time 

period defined by L. 

It is important to visualize the relative value of each of these parameters in a typical 

Smart Grid network for a single actor. This is illustrated in Figure 39. 

Figure 39 - Relationship between L, TMSG, and TPKT

As illustrated in Figure 39, TPKT < L < TMSG. This relationship assures that L/TMSG < 1

and L/TPKT >1. 

TMSG TPKT = 8(PAVG/CGP)

L
106 

The number of actors that can be supported by a channel can be estimated by using the 

Cumulative Binomial Distribution Function27. The probability that the offered load in a 

given time window, L, is less than the channel goodput is calculated as follows: 

l
u; $, g = x
9	 ≤ u 	 = 	#z

$

{

| gN


1 − g

&N

}~

Nt2

Where 

x = The maximum number of transmission events in a window = largest integer ≤ L/TPKT

n = Number of actors 

p = Probability of an actor transmitting in the window = PMSG = L/TMSG

It should be noted in this analysis we are calculating the probability that the offered load 

in the time period, L, does not exceed the goodput capacity of the channel or network 

segment. This does not mean that if this capacity is exceeded for a short period of time 

the overall transaction latency requirement or even the fraction of this latency allocated to 

this segment will be violated. It only indicates the probability that the offered load may 

exceed the goodput capacity of the network segment during the period, L. Even if the 

goodput capacity of the network segment is exceeded during time period, L, and packets 

start to queue up, there is still a finite probability that each packet may leave the queue 

and be transmitted through the network segment within its allocated latency allotment. 

However, this analysis is a good way to illustrate the probability of overloading the 

capacity of a network segment which may well result in a significant violation in the 

overall latency requirement for the transactions. 

The results are shown in Figure 40 and Figure 41 for an average channel goodput of 1.0 

Mb/s and 0.1 Mb/s (100 kb/s), respectively. For these two examples the message rate, 

RMSG, is assumed to be 300 messages per hour which translates to an average time of 12 

seconds between messages and the average packet size in both cases is assumed to be 250 

bytes. In these figures the confidence values refer to the probability that the load offered 

by the number of nodes or actors will not exceed the goodput capacity of the network 

segment during a period defined by L, the allocated portion of the overall transaction 

latency requirement. 

 

27 In Excel® the expression is: BINOMDIST(L/TPKT, # Actors, PMSG, 1) 
107 

Figure 40 - Actors per channel for goodput = 1.0 Mb/s 

Figure 41 - Actors per channel for goodput = 0.1 Mb/s 

Table 17 provides a summary of the expected change in the number of actors that can be 

supported per channel for variations in the relevant parameters. The desired confidence 

level in all cases is assumed to be 99.5 %. 
108 

Table 17: Summary of expected change 

Parameter Nominal Value Parameter Change Change in number of 

Actors 

Latency (L) 1.0 second - 50 % - 5.5 % 

Channel goodput (CGP) 1.0 Mb/s - 20 % - 22 % 

Packet size (PAVG) 250 bytes + 20 % - 17 % 

Message rate (RMSG) 300 message per 

hour + 20 % - 16 % 

The number of actors that can be supported by a given channel is primarily dependent on 

the channel goodput which in turn is a function of the available bandwidth and the total 

channel overhead. A less obvious result is the fact that the value, L, used in this analysis 

has a relatively small effect on the number of actors that can be supported through a 

network segment. This is because the probability of a successful trial is more directly 

proportional to congestion of the network segment than to the time window size, L, used 

to calculate the binomial distribution values. 

5.2.7.2 M/D/1 and M/M/1 Queuing System Models 

Another approach for modeling the delay encountered in a network segment is based on 

the M/D/1 and M/M/1 mathematical models. These models have been used for general 

service time analysis and, when applied to networking systems, they are commonly used 

for response time and throughput analysis. 

In the networking context considered here, the M/D/1 is referred to as a constant service 

model. It refers to a mathematical model where packet arrival rates are random, 

described by a Poisson process (sometimes also referred to as having negatively 

exponentially distributed inter-arrival times), and where service or transmission time 

through the network segment is constant. A further assumption is that there is only one 

network transmission path or segment through which all the payload packets will transit. 

These assumptions are reasonable for an analysis where each downstream node offers 

payload data packets at a fixed average rate, each packet is a constant fixed size, and the 

packets arrive from the multiple downstream nodes in an independent and random 

manner. This model requires two basic inputs; the average combined arrival rate of 

packets per unit time (λ)

28 and the number of packets that can be transmitted per unit 

time, which is also known as the service rate (μ). For a stable system λ, the arrival rate, 

must be less than μ, the service rate. If a sustained arrival rate is greater than the service 

rate, the queue will grow without limit. The arrival rate of packets is in-turn related to 

the average rate packets are generated by each node and the number of downstream nodes 

feeding into the network segment. It is calculated by simply multiplying the number of 

active downstream nodes by the average rate packets are generated by each node. The 

packet service rate is calculated by simply dividing the network segment goodput rate, 

CGP, (in b/s) by the number of bits in each packet. This yields the service rate in packets 

per second. Since the size of the transmitted packets in these examples is considered 

 

28 λ is used here to be consistent with the terminology typically used in describing the M/D/1 or M/M/1 

models. This is the same as RMSG for the Binomial Distribution Model 
109 

constant (250 bytes each) and the goodput is also assumed constant for each example 

considered, the calculated service rate for each example is also constant, as required for 

the M/D/1 analysis. 

In the forgoing M/D/1 and M/M/1 analysis two examples are considered. One 

considering the goodput of the network segment is 0.1 Mb/s, and the other for a goodput 

1.0 Mb/s. This matches the assumptions in the previous analysis the Binomial 

Distribution Model, namely: 

• Packet Size = 250 bytes (or 2,000 bits) 

• Packet arrival rate per node = 300 packets per hour or equivalently 0.083333 

packets per second per node 

• For a goodput of 0.1 Mb/s the service rate (μ) is calculated by dividing 100,000 

(b/s) by 2,000 bits per packet which equates to 50 packets per second. 

• For a goodput of 1.0 Mb/s the service rate (μ) is calculated by dividing 1,000,000 

(b/s) by 2,000 bits per packet which equates to 500 packets per second. 

In considering the number of nodes or actors that can be supported by a network segment 

of a fixed capacity or goodput, we must consider the combined traffic load offered by 

these nodes. This can be easily calculated by multiplying the packet arrival rate from 

each node by the number of nodes. The result is the total offered load, λ, in packets per 

second. 

The equation for calculating the expected average time (Ert) a packet takes to be 

processed completely through the communications channel or network segment for the 

M/D/1 analysis is: 

Ert = (2-ρ)/((2×μ×(1-ρ)) 

where 

ρ = λ/μ = packet arrival rate per service rate. 

In this example the combined average arrival rate (λ) is calculated by multiplying the 

number of nodes offering traffic (N) times the arrival rate from each node, or N×.083333. 

The value of μ = 50 for the 0.1 Mb/s goodput example, and μ = 500 for the 1.0 Mb/s 

goodput example as pointed out above. 

Thus the value ρ = arrival rate per service rate = N nodes × 0.083333/50 for 0.1 Mb/s 

goodput and the value ρ = arrival rate per service rate = N nodes × 0.083333/500 for 1.0 

Mb/s goodput. 

These calculations can be easily implemented in a spread sheet using, as input variables; 

the number of nodes or actors, the packet arrival rate from each node, and the service rate 

at which packets can be transmitted through the network segment at the given goodput 

rate. In a spread sheet analysis, the service rate would actually be calculated by dividing 

the size of the packets (in bits) into the goodput rate (in b/s). 
110 

In contrast to the M/D/1 model, the M/M/1 model is generally considered to be more 

conservative when estimating the effective capacity of a network segment which may, inturn, lead to an underestimation of the number of nodes that can be effectively served 

through a network segment. In the M/M/1 model used in the forgoing examples, the 

assumed packet size is no longer considered a constant but instead its average size is 

considered to be 250 bytes but is expected to vary according to an exponential 

distribution. Thus the calculated service rate for these packets also follows an 

exponential distribution. When considering both an M/D/1 and M/M/1 analysis, they 

may together serve to bracket a more realistic number of nodes that can be effectively 

served by a network segment, with the M/D/1 over-estimating and the M/M/1 underestimating the number of nodes that can be effectively served by a network segment of a 

given channel goodput. To illustrate, consider the M/M/1 model described here where all 

the variables used in the calculations remain the same as in the previous M/D/1 example, 

except the size of the packet is now assumed to vary according to an exponential 

distribution. 

The equation for calculating the expected average time (Ert) a packet takes to be 

processed completely through the communications channel or network segment for the 

M/M/1 analysis is: 

Ert = (1/μ)/(1-ρ) 

As before, for the M/D/1 analysis, the value ρ = λ/μ (packet arrival rate per service rate). 

In this example the combined average arrival rate (λ) is also calculated by multiplying 

the number of nodes offering traffic (N) times the arrival rate from each node, or 

N×0.083333. Thus the value of μ = 50 for the 0.1 Mb/s goodput example, and μ = 500 

for the 1.0 Mb/s goodput example. 

Therefore, the value ρ = arrival rate per service rate = N nodes× 0.083333/50 for 0.1 

Mb/s goodput and the value ρ = arrival rate per service rate = N nodes× 0.083333/500 for 

1.0 Mb/s goodput. 

Using the stated input parameters, and the equation for Ert, the following two charts, 

Figure 42 and Figure 43, of the expected average time for a packet to be transmitted 

through the network segment vs. the number of active actors offering packets to be 

transmitted, can be constructed for the M/D/1 (constant service time) and M/M/1 

(exponential service time) models. The charts clearly show the more conservative 

estimate for the M/M/1 model compared to the M/D/1 model. 
111 

Figure 42 - Average packet latency for goodput = 0.1 Mb/s 

Figure 43 - Average packet latency for goodput = 1.0 Mb/s 

In calculating the expected average time (Ert) a packet takes to be processed completely 

through the communications channel or network segment, it is important to note the 

M/D/1 and M/M/1 analytical models used in this example take into account the 

probability that packets arriving at the network segment for transmission may not be 

immediately transmitted due to channel congestion. In that case they are placed in the 

queue of packets awaiting their turn to be transmitted. This additional queuing delay is 

obvious in Figure 42 and Figure 43 as the number of nodes increases such that the 

combined offered load approaches the total capacity or goodput of the network segment. 

These two charts show the analysis of capacity vs. latency in a different context than the 

Binomial Distribution Model described earlier, which calculated the probability 

distribution for the number of packets arriving within a given time window of size, L, that 

would exceed the maximum capacity for transmitting these packets in that same time 

window, L, where L was selected to be equal to the required latency. These latter charts 

(Figure 42 and Figure 43) however, dramatically show that as the number of nodes 
112 

increases, the offered load to a network segment increases, the effects of congestion 

quickly cause the average delay for each packet to lengthen significantly as the offered 

load to the network segment approaches its maximum capacity. 

Again, the reader is cautioned that the preceding M/D/1 analysis is based on average 

response time for a fixed set of parameters, namely in this case each message or packet is 

250 bytes in size and the arrival rate is assumed constant over time at 300 packets per 

hour per node. In practice, the assumption of constant packet size and the resulting 

constant packet service rate is generally not realistic and may lead to over-estimating the 

number of nodes that can be effectively served through a network segment of a given 

goodput. The M/M/1 analysis on the other hand, can be considered as being a more 

representative model for simulating what would be encountered in practice. 

One could use charts similar to Figure 42 and Figure 43 as a guide for estimating the 

maximum number of nodes or actors that can be effectively served by a network segment 

to ensure the average packet latency encountered in the network segment will lie between 

the two lines on the chart. By identifying the fractional portion of the overall transaction 

latency that can be apportioned to the network segment in question, using the charts 

illustrated here can provide guidance as to sizing the maximum number of nodes or actors 

that can be served by a network segment while ensuring the average packet latency will 

not exceed the allocated maximum value. 

5.2.7.3 Comparing the Binomial Distribution Model with the M/M/1 Model 

Two different approaches have been discussed for determining a network segment’s 

ability to meet a desired latency. Both approaches are probability-based and thus will 

only provide an estimated value with some degree of confidence as to the number of 

supportable nodes or actors that can be supported as the network segment, or in this case 

a channel, approaches a congested state. 

The use of either the binomial distribution analysis technique or the M/D/1 or M/M/1 

analysis techniques will provide an estimate of the capacity of the link to meet the 

required latency for that segment. Using the binomial analysis technique described here 

will lead to an estimate of the probability for meeting the allocated latency, whereas the 

use of the M/D/1 and M/M/1 analysis techniques will provide an estimate of the average 

time to transmit a packet through the network segment. Both techniques are useful in 

evaluating the ability of the network segment to satisfy its latency requirement and how 

this ability is related to its goodput and the load to which it is being subjected. 

To gain further perspective for the applicability of either of these models for predicting 

the latency performance in a Smart Grid network segment, it is informative to see how 

the two models compare. 

The predictions for the number of supportable actors for the M/M/1 and Binomial 

Distribution models are shown in Figure 44 and Figure 45 for 97.5 % and 95 % 

probability respectively. As the charts illustrate, the Binomial Distribution approach 

provides a more conservative prediction than the M/M/1 approach which in turn, as 
113 

discussed in the previous sections, provides a more conservative estimate than the M/D/1 

model. 

Figure 44 - Comparison for 97.5 % probability 

Figure 45 - Comparison for 95 % probability

5.2.7.4 Additional Latency Considerations and Conclusions 

While the examples described in this section and their analysis may seem complex, 

unfortunately in the real world, one would expect the offered load to be even more 

complex than assumed in any of the preceding analysis. Transactions and packets 

transiting a network segment would likely consist of a mixture of transaction types, 

packet sizes, and the arrival rates and in this mixture is likely to vary dramatically over 

time, particularly for periodic events like meter reading. Other more complex 

mathematical models beyond those illustrated here may be employed to handle these 

more complex examples. While beyond the intent and scope of this rudimentary latency 
114 

and response time analysis, an interested reader may wish to explore these more complex 

models further by exploring the subject of response time analysis on the Internet or by 

consulting a number of text written on the subject. However, the examples presented 

here illustrate the basic concepts and principals in analyzing the latency and capacity of a 

network segment and how these relate to the number of nodes that may be effectively 

served through that network segment. 

For a more complete picture of the overall network delay, an analysis like this would 

need to be conducted on each network segment, and for each particular transaction type 

traversing it, and in each direction for the two-way or round trip application response 

time consideration. However, as each different network segment may have a different 

mixture of application transactions with each at different rates and then when combined 

will provide different congestion values, it can be seen a complete and comprehensive 

analysis would be a very complex problem indeed. Without the aid of a sophisticated 

computerized modeling system to provide a more comprehensive analysis, it is suggested 

one would look at the low bandwidth (or low goodput) network links and at the most 

congested links in a network and evaluate these further to determine if the congestion 

delay and the transmission delay through these links would be likely to cause the 

transaction latency values to be violated. 

In conclusion, as stated earlier the models described in this section do not account for all 

of the contributors to network latency. A more complete analysis would include the 

delays required to initiate a session and fully process the data packets at each of the nodes 

in the transmission path and may also include, for longer physical path lengths, the 

propagation delay. To summarize, these additional contributors to latency are: 

• Time to initiate a session from idle or sleep mode to active data session mode, this 

includes authentication and admission control 

• Time required to process packet headers and determine where packets should be 

routed 

• Time required to initiate a connection with an alternate BS that is within range 

(BS to BS handover) during periods of changing propagation conditions or for 

mobile applications 

• Propagation (OTA) time 

The propagation time is 3.3 μs / km and for any terrestrial network can be ignored. It 

may be a factor in satellite systems, however. The other three contributors are generally 

in the 10 ms to 100 ms range and may be ignored for most cases but could become 

significant when mission-critical data is transmitted over a multi-hop path. In those cases 

one could choose to simply add a reasonable value for each node; 25 ms to 50 ms would 

probably be sufficient to capture the average impact. Another scenario for which this 

contribution can be a factor is when the latency requirement for very large application 

payloads is apportioned to much smaller packets for transmission. 

Another important factor not taken into consideration with any of the models is QoS. All 

of the wireless submissions for outdoor terrestrial networks have some support for setting 
115 

packet priorities, an essential ingredient of QoS. This enables the prioritization of 

individual data packets with respect to their tolerance to latency. 

Obviously, to account for all of these factors with a simple, easy to use, mathematical 

model for wireless network planning purposes would be a major undertaking. Despite 

the limitations, any of the models described in this section can prove useful in assessing a 

channel’s ability to meet Smart Grid latency requirements when the channel is in a 

congested state, when queuing delays will tend to dominate. The latency performance 

based on the model will predict a conservative result since, when QoS features are taken 

into account, the performance will only improve for high priority latency-sensitive 

payloads. 

 
116 

6 Practical Considerations in the Deployment of Wireless Networks for SG 

Applications 

Section 4 provided a detailed description of the various attributes and performance 

parameters that would be important in making an assessment of how different wireless 

access technologies would apply in a Smart Grid communications network. Section 4 

also provides a link to the Wireless Functionality and Technology Matrix which provides 

a summary of performance details for several wireless access technologies as submitted 

by Standards Development Organizations. 

In section 5, a number of propagation and path loss models were presented along with 

various graphs, tables, and other models and relevant information that would be 

applicable to a land-based wireless technology deployment. A special effort was made in 

this section to take into account the specific deployment requirements and trade-offs that 

are applicable to Smart Grid applications as opposed to traditional cellular networks. 

The goal for this section is to build on what was presented in section 4 and section 5 and 

take into account some of the varied challenges and trade-offs that will likely be 

encountered in a typical Smart Grid communications network deployment. In section 

6.5, an Excel®29-based tool is introduced. This tool is intended to provide a means for 

quantitatively assessing alternative terrestrial-based wireless solutions in deployment 

regions with varied demographic and propagation characteristics based on average Smart 

Grid network uplink and downlink payload requirements. 

Specifically, this section is structured as follows: 

• Section 6.1: Coverage, Capacity, Latency Trade-offs

• Section 6.2: Advanced Antenna Systems and Spectrum Considerations 

• Section 6.3: Multi-Link / Multi-Hop / Mesh Topologies 

• Section 6.4: Addressing the Challenges with Multi-Tenant High Rise Buildings 

• Section 6.5: SG Framework and Wireless Modeling Tool 

• Section 6.6: Interoperating and Interworking with Other Wireless Technologies 

• Section 6.7: Assessment of Modeling Tool Results 

• Section 6.8: Cross Wireless Technology Considerations 

6.1 Coverage, Capacity, Latency Trade-offs 

This section discusses key performance factors that are common to any smart grid 

wireless communication network deployment and how these factors relate to the 

demographics and characteristics of the area being considered for deployment. From an 

operational perspective key performance parameters are propagation range, UL and DL 

 

29 Any mention of commercial products within this report is for information only; it does not imply 

recommendation or endorsement by NIST. 
117 

channel capacity, and latency. In section 5 we discussed and provided generally accepted 

path loss models for indoor and outdoor land-based wireless networks. Additionally we 

described how link budgets can be derived and how range and channel capacity can be 

determined. In this section we bring in the other key deployment variable: demographics. 

In a wireless network we can generally describe deployments as Range-Limited or 

Capacity-Limited. Range-limited scenarios cover the case where each BS is deployed in 

a manner that fully utilizes its range capability determined solely by the applicable link 

budget and the path loss characteristics of the area being covered without regard to data 

capacity requirements. Capacity-limited describes scenarios for which data traffic 

requirements are high and BSs or APs have to be spaced closer together to limit the 

number of actors per BS so as not to exceed the BS capacity capability. 

Latency is another key SG performance requirement and depending on; channel goodput, 

average message size and rate, and number of actors, could result in a deployment that is 

limited in its ability to meet latency requirements in accordance with the model that was 

described in section 5.2.7. In addition to the channel access delay predicted by the 

model, it may, in some cases, be necessary to account for node processing delays. These 

would account for encryption / de-encryption, error detection and correction, etc. 

Generally these are small enough to be neglected but may come into play with large 

latency-critical payloads. The remaining contributor to delay is propagation (OTA) 

delay, 3.33 μs / km. This can be ignored for terrestrial wireless networks but can be a 

factor with satellite links. 

6.1.1 Demographic Breakdown 

From a demographics perspective it is informative to group deployment regions into the 

five categories described in Table 18 which includes area breakdowns based on US 

census data30. 

Table 18: Demographic breakdown 

Demographic 

Region 

Housing Unit 

(HU) Density 

 (housing unit per 

square mile) 

% of US 

Population 

% of 

US 

Land 

Area 

Typical Characteristics 

Dense Urban ≥ 4,000

(≥1545 housing 

unit per square 

kilometer) 

11.0 0.05 Large number of high rise multi-tenant 

buildings large number of businesses 

Urban 1,000 to 3,999 34.7 0.6 Densely packed 4-6 story buildings, 

residential and industrial 

Suburban 100 to 999 30.7 3.2 Mix of 1 and 2-family homes, low rise 

apartment buildings, shopping centers, 

more trees, parks, etc. 

Rural 10 to 99 17.0 22.7 Larger parcels, low rise buildings, more 

trees and terrain obstacles 

 

30 The data for area breakdowns are from the US 2010 Census data which is based on square miles 
118 

Demographic 

Region 

Housing Unit 

(HU) Density 

 (housing unit per 

square mile) 

% of US 

Population 

% of 

US 

Land 

Area 

Typical Characteristics 

Low Density 

Rural 

< 10

(<4 housing unit 

per square 

kilometer) 

4.2 72.3 More extreme terrain characteristics, 

HU densities vary from clusters to 

individual HU miles apart 

In addition to the typical area characteristics included in the table there are some 

additional generalizations that can be made related to population and housing unit (HU) 

densities that relate directly to terrestrial wireless SG network deployments. 

In deployment regions with very high population densities one can expect: 

• Limited spectrum options: Spectrum congestion will always be a limiting factor in 

areas with high population density. These are prime markets for other wireless 

operators and if any excess network capacity does exist, it will very likely be 

quickly consumed to keep up with the growing demand. That said, any spectrum 

that is available for Smart Grid networks may be in limited amounts and may not 

always be in a favorable frequency band for best range and coverage. This may 

require the use of smaller channel BWs, subsequently leading to lower channel 

capacity. 

• Higher interference: With higher traffic densities and smaller cell sizes the 

potential for interference will be higher in these regions. This will be especially 

true in the unlicensed bands but can also play a role in licensed bands unless 

generous guard-bands are used. Limited spectrum dictates more aggressive 

frequency reuse, giving rise to greater sector to sector and cell to cell interference. 

The need for greater margins to account for interference will lower the link 

budget. 

• Most deployments will be capacity-limited or limited in the number of actors per 

channel to meet latency requirements. Limited spectrum and high HU densities 

will lead to deployments that will have to be sized to meet capacity and latency 

requirements for most SG network segments. 

In contrast, for areas with very low population densities it is reasonable to expect: 

• Spectrum availability: Spectrum is more likely to be available. Existing license 

holders in some cases will be willing to lease portions of underutilized blocks of 

spectrum. Sharing the Public Safety bands with local municipalities is a realistic 

expectation and the use of license-exempt spectrum can be considered without the 

concern for large amounts of interference. 

• Most deployments will be range-limited: With the ability to deploy a wireless 

network with a reasonable channel BW, deployments in rural areas will most 
119 

always be limited by the range capability. The exception would be in the 

unlicensed bands where regulators impose EIRP restrictions. 

It is informative to delve deeper into the two extreme demographic categories, Dense 

Urban and Low Density Rural, to gain a better understanding of the challenges associated 

with each with respect to an SG wireless network. 

6.1.2 Dense Urban Regions 

In addition to the high density of meters and other utilities infrastructure that must be 

connected in a Dense Urban area wireless network, the deployment must also deal with 

significant propagation challenges. With the prevalence of underground utilities in 

metropolitan areas, meter banks will often be located in grade-level weatherized 

enclosures or below ground level in the basements of high-rise multi-tenant buildings. In 

either case the penetration losses will be significant. Additionally, as was discussed in 

section 5, all of the relevant path loss models predict a higher path loss for urban 

deployments as compared to average suburban and rural areas due to building blockage. 

The range capability is further impacted when BS antennas are located below the roof 

tops of the surrounding buildings. This is clearly illustrated in Figure 46, where range 

projections are shown for an AMI network under the following conditions: 

• Outdoor pole-mounted DAP (BS) with an antenna height of 10 m (denoted as OD 

Pole) 

• Basement located or cabinet-enclosed meter banks (denoted as ID Basement) 

• Above ground located meter banks in indoor locations (denoted as ID Other) 

The range projections are based on the ITU-R M.2135-1 Urban Micro-cell path loss 

model described in section 5.2.1.3.4. This model is considered valid for the 2000 MHz to 

6000 MHz frequency range. The dotted lines extending the data to 700 MHz are simply 

estimated projections for illustrative purposes. 

Figure 46 - Dense urban range projections 
120 

The third plot in Figure 46 is based on the ITU-R M.2135-1 Large City Urban path loss 

model and assumes: 

• An outdoor roof mounted BS (30 m height) 

• Terminals consisting of outdoor pole-mounted DAPs (10 m height) 

What can be surmised by this is that for AMI deployments in Dense Urban and probably 

many Urban area deployments, the deployment will very often be range-limited and not 

capacity-limited since the coverage area will be severely limited by the high penetration 

loss to reach installed meter banks and the higher urban area path loss with DAPs 

mounted below adjacent roof tops. With the small coverage area the number of meters 

per DAP will generally be well within the capacity capability of the BS. At 3500 MHz, 

based on 4000 housing units per square mile, the traffic load for each sector would be 

about 300 smart meters per channel. It is important to mention that mounting the DAPs 

above the prevailing roof height in a dense urban area would not yield a significant 

benefit. Since the DAP antennas, with a fan-shaped beam, would be pointed downward 

to reach the grade or below grade located meters, the effective area coverage would not 

be increased enough to offset the added complexity and cost of acquiring roof-rights. 

The above analysis suggests a layered architecture for dense urban areas as shown in 

Figure 47. The pico-cells and micro-cells represent DAPs with a nominal antenna height 

of 10 m. The pico-cells access basement-located and vault-enclosed smart meters while 

the micro-cells have access to smart meters in more favorable locations from a path loss 

perspective and, with lower penetration loss, cover a wider area. The macro-cell provides 

an aggregation node for the DAPs and may also pick up additional smart meters within 

its wider coverage area. The macro-cell would have to handle the combined data traffic 

and in most cases will be capacity-limited. 

Figure 47 - Layered architecture for dense urban 

Micro-cell

Pico-cell

Pico-cell Pico-cell

Macro-cell

Coverage Area 

(Capacity-Limited)

Micro-cell

Pico-cell

Pico-cell

Backhaul 
121 

6.1.2.1 Dense Urban Latency Considerations 

As mentioned earlier, a model for assessing the network’s ability to meet latency 

requirements was described in section 5.2.7. The model applies to a single link with a 

known average channel goodput. The same model can be used for a layered network 

architecture provided the latency allocated for the network is properly apportioned to the 

link being analyzed. For an end-to-end latency requirement of LN and the data path 

illustrated in Figure 48, each link must be designed to meet a latency of LN/4. The 

probability (or confidence) factor must also be apportioned between the multiple links. 

For an end-to-end requirement of 99 % and four links, each link would be required to 

meet 0.991/4 = 0.9975 (99.75 %). 

Figure 48 - Latency with layered architecture 

The AMI network will have a greater number of actors with small data packets. The 

DAP backhaul network, on the other hand, will be supporting fewer actors but data 

payloads will be larger. Even with the same channel BW, the average channel goodput 

will typically be higher for the macro-cell since, in most cases, it will be capacity-limited 

or limited in its ability to meet latency requirements. 

6.1.2.2 Relating Channel Capacity and Latency 

In the design of any communications network the decision that must always be addressed 

is whether to design to meet average or peak busy-hour demand. Sizing the network for 

average demand reduces network cost but means that reduced performance will have to 

be tolerated during periods of peak demand. For a telephony network it generally 

translates to a higher probability of a ‘busy signal’, for a Smart Grid network it may 

result in higher average latency. Designing the network to meet peak demand increases 

network cost and may result in a network that has excess capacity for a large percentage 

of time. For a Smart Grid network highload, driven by large payload firmware upgrades 

and other special events, can be orders of magnitude greater than baseload. 

Even though the pico-cell deployment in the AMI network will, in most cases, be rangelimited due to the high penetration losses, highload traffic conditions, especially in dense 

urban areas, may approach the channel capacity limit. 

Since the AMI network is likely to be operating with limited spectrum and link latency, 

requirements may be quite stringent due to the multiple links or hops in the end-to-end 

communications path. It is important to understand the relationship between the channel 
122 

data capacity and the link latency. As was described in section 5.2.7, the latency 

prediction is based on the channel goodput (CGP), the average packet size (PAVG), and the 

message (or packet) rate RMSG. 

If, as an example, we assume a CGP of 1.0 Mb/s and a peak data rate per actor or endpoint of 100 b/s (approximately 12 bytes per second), the channel capacity at peakload 

would be 10,000 actors per channel. The actor message rate, RMSG, under these 

conditions is 0.05/s or 180/h. 

Figure 49 shows the probability of meeting a particular link latency requirement as a 

function of the actor load on the channel using the Binomial Distribution Model 

described in section 5.2.7. With 8500 actors (85 % of the channel actor capacity) there is 

a 99.5 % probability that a packet will meet a 0.5 second latency requirement or, 

alternatively, less than 0.5 % of the packets will exceed a latency of 0.5 seconds. This 

value is about 10 % at 92.5 % of the channel capacity and grows to over 30 % when the 

channel is loaded to more than 97.5 % of capacity. If the channel were operating at full 

capacity the latency would be about 20 seconds. 

Figure 49 - Probability of meeting latency requirement 

Another way to present this data is shown in Figure 50, where the channel load as a 

percentage of channel capacity is plotted versus the latency prediction for different 

probabilities. This representation may prove more useful as a network design or planning 

tool when one is faced with the decision of dimensioning the network capacity to meet 

baseload, average load, or highload requirements. 
123 

Figure 50 - Latency relative to channel loading 

For this particular example, if one wanted to maintain a latency of 0.5 seconds with a 

probability of 99.5 % during periods of highload, it would be necessary to design for a 

channel capacity 15 % higher. If, on the1 other hand, a latency of 3.0 seconds with a 

probability of 95 % could be tolerated during peakloads, one could operate up to 96 % of 

the channel capacity. 

Figure 50 is applicable for this particular example and would have to be re-plotted for 

different parameter assumptions. Generally a reduced packet size would support higher 

channel loading for a given latency. Although a smaller packet size increases the 

message rate (RMSG), the probability that a packet falls within the specified latency 

window increases. Bear in mind that smaller packet sizes will also increase the channel 

OH so one can also expect a slight reduction in channel goodput. 

This analysis does not take into account any of the QoS features that are supported by 

most wireless technologies. The analysis assumes all packets are treated with equal 

priority. With QoS support it would be possible to assign higher priorities to packets that 

are more latency-sensitive while relegating latency-tolerant packets to a best effort status. 

From the standpoint of evaluating the channel’s ability to meet latency requirements, 

with QoS, the best effort packets would be ignored.

6.1.3 Low Density Rural 

The opposite extreme to Dense Urban is the area designated as Low Density Rural. 

According to the United States census data over 4 % of the US population lives in over 

70 % of US land area. With housing unit densities less than 10 HU per mi2

 (< 4 

HU/km2), deployments in these regions will always be range-limited with any reasonable 

amount of spectrum. The key challenge for a terrestrial wireless network deployment is 

to optimize the coverage so as to reach all housing units and enterprise units that are 

connected to the electrical grid. Special attention has to be paid to: 
124 

• Terrain characteristics that can vary from flat wide-open spaces to rugged

mountainous terrain with high tree density. 

• Long distance wireless backhauls that may require daisy-chained point-to-point 

(PtP) links or a satellite link for connectivity to the command center. 

Path loss models for foliage (5.2.1.3.7) and path obstructions (5.2.1.3.8) were presented 

in section 5.2.1.3 and a modified version of the Erceg-SUI path loss model was shown to 

be an effective path loss predictor for various terrain categories in rural and suburban 

areas over the full 700 MHz to 6000 MHz frequency range. A description of the three 

terrain categories are repeated here for convenience: 

• Terrain Type A: Hilly with moderate to heavy tree density 

• Terrain Type B: Hilly with light tree density or flat with moderate to heavy tree 

density 

• Terrain Type C: Flat with light tree density 

Although not all terrain types that are likely to be encountered throughout North America 

can be fit into one of the above types, it is believed that these three terrain types can be 

used to describe a large majority of Low Density Rural deployment scenarios. For 

extreme terrain conditions other approaches, as noted in section 5.2.1.3.11, may have to 

be employed to provide a more accurate estimate. 

Figure 51 provides the coverage area projections for the three terrain types for an AMI 

network for the 700 MHz to 6000 MHz frequency range. The link budget of 143 dB31 in 

the UL direction is the limiting factor for the range determination due to the lower EIRP 

and antenna gain for the wireless enabled smart meter. The DAP antenna height is 

assumed to be 20 m with a gain of 15 dBi. This height should not be unreasonable in 

rural areas where existing transmission towers would be logical candidates for BS 

locations. 

 

31 Assumes 14 dB fade and interference margin and outdoor-located smart meters with 0 dBi antenna gain 
125 

Figure 51 - AMI coverage area projections 

In Figure 52 the sensitivity of the coverage area is plotted for different antenna heights 

for those locations where a 20 m height is impractical. The chart also shows the benefit 

of higher heights for situations where suitable structures or standalone towers are 

available. This data is plotted for a frequency of 2000 MHz but the results are not 

significantly different at either 700 MHz or 6000 MHz. With its large impact on 

coverage area, BS antenna height will play an especially big role in determining 

equipment requirements for SG networks in low density rural areas. 

Figure 52 - Sensitivity to BS antenna height 

The second key challenge in low density rural areas is the backhaul connection which 

may require multiple PtP links for a land-based solution or the possible use of a satellite 

link or some combination of the two. A typical Low Density Rural or Rural wireless 

network architecture may resemble what is shown in Figure 53. 
126 

Figure 53 - Typical network architecture for low density rural 

As shown in Figure 53 the end-to-end data path will very often encompass multiple links 

or hops, each with very different characteristics. As was the case with dense urban, the 

network latency budget should be properly apportioned to each individual link before 

applying the latency model described in section 5.2.7. If a satellite link is employed, the 

propagation (OTA) delay should also be considered. When end-to-end latency is the 

limiting performance factor, fewer terrestrial long links will be preferable to a higher 

number of short links for reducing latency. This replaces node processing delay with 

propagation delay. Longer links, of course, will generally require increased antenna 

heights and higher EIRPs. 

6.1.4 Summary 

Urban area path loss and high penetration losses will significantly limit the range and 

coverage for a wireless AMI network. In most cases these deployments will be rangelimited pico- and micro-cells. With roof mounted BS antennas to provide a backhaul 

connection for the DAPs, the coverage potential will be much greater but capacity 

requirements to handle the aggregated traffic will ultimately determine the coverage not 

the range. 

Low density rural area deployments will be primarily range-limited. The use of the lower 

frequency bands and deploying BS heights of 20 m or more will greatly reduce 

equipment requirements. A combination of PtP links and satellite links may prove to be 

the best choice to fulfill backhaul requirements in remote areas but with an increasing 

number of links, latency could become a limiting factor. 

The various trade-offs and considerations for deployments in Dense Urban and Low 

Density Rural areas should provide some insights into the factors that must be considered 

in the other three demographic regions described earlier; Urban, Suburban, and Rural. 

Urban areas would still have the building clutter to deal with but, with lower average 

building heights, slightly better propagation characteristics. Deployments, however, will 

still tend to be capacity-limited. 

Residential suburban and rural areas will not generally be able to accommodate the 

higher BS antenna heights due to visual impact and the limited height of utility poles that 

would often be the preferred choice for BS locations. Depending on channel BW 
127 

limitations imposed by limited spectrum availability and HU density, suburban areas may 

be either range-limited or capacity-limited. 

6.2 Advanced Antenna Systems and Spectrum Considerations 

Advanced antenna systems have become commonplace in today’s wireless networks; 

even indoor WiFi APs are often equipped with multiple antennas. These advanced 

antenna systems can be grouped into two generic categories designated as Multiple Input 

Multiple Output (MIMO) and Beamforming (aka Phased Arrays). Each of these generic 

categories has different attributes that translate to improved range and coverage, 

improved channel capacity, higher availability, or a combination of the three. 

6.2.1 Multiple Input Multiple Output (MIMO) Antennas 

MIMO systems are described as illustrated in Figure 54, where transmit antennas are 

designated as inputs to the OTA channel and receive antennas are receptors of multiple 

input paths. A BS equipped with NT transmit (Tx) antennas and NR receive (Rx) antennas 

would therefore be described as having a (NT × NR) MIMO antenna configuration. A 

configuration with 1 Tx antenna and 2 Rx antennas would be designated as having a 

(1×2) Single Input Multiple Output (SIMO) antenna configuration. 

Figure 54 - Antenna nomenclature 

To take maximum advantage of the performance attributes that MIMO antenna systems 

can support, it is necessary to minimize correlation between antennas. This can be 

accomplished through spatial separation, typically 3 wavelengths to 5 wavelengths or 

more. With only two antennas, cross polarization can be used. 

With multiple Tx antennas, MIMO systems can generally operate in one of two transmit 

modes and in most cases can auto-adapt to the mode most applicable to existing channel 

conditions at any given time. These transmit modes are called: 

• Transmit Diversity 

• Spatial Multiplexing 

Transmit diversity describes a scenario whereby the same data stream is transmitted over 

each of the transmit antennas. This provides multiple independent transmit paths thus 
128 

increasing the probability of a satisfactory reception at a distant terminal. This feature 

translates to an increase in system gain resulting in either an increase in range or an 

increase in availability. Assuming there is no correlation between antennas, a system 

with 2 Tx antennas will result in a 3 dB system gain increase and a 4 Tx antenna system 

will result in a 6 dB increase. 

With spatial multiplexing each Tx antenna transmits a different data stream to effectively 

increase the channel capacity. This can provide up to a two-fold increase with two Tx 

antennas and up to a four-fold increase with four Tx antennas. 

As a practical matter the performance gains for Transmit Diversity or Spatial 

Multiplexing will be somewhat less than theoretically predicted due to variations in 

multipath, antenna patterns, and mounting limitations. 

Multiple Rx antennas in MIMO systems generally support: 

• Receive Diversity with or without Maximal Ratio Combining (MRC) 

• Multi-User MIMO (MU-MIMO) 

Receive Diversity enhances link reliability by providing multiple independent receive 

paths. In its simplest implementation the receiver simply selects the highest signal level 

from one of the multiple antennas. Since a deep fade is unlikely to occur simultaneously 

on each path the probability of receiving a signal above the threshold level is increased. 

With Maximal Ratio Combining (MRC) the received signals are combined to provide a 

received signal level higher than any of the antennas receive individually. 

Multi-User MIMO (MU-MIMO) is a technique typically employed on the receive side at 

the BS. With multiple Rx antennas, this approach combines transmissions from multiple 

terminals to increase the BS UL channel throughput.

6.2.2 Beamforming and Beam steering 

Beamforming is another advanced multiple antenna option that is available with many 

terrestrial-based wireless systems. This approach requires each BS antenna in the array 

to be spaced one-half wavelength apart. With proper phasing and amplitude control the 

resulting antenna pattern is formed into a narrow beam that can be steered (beam 

steering) to direct the beam to different areas within a sector coverage area as illustrated 

in Figure 55. The beamwidth is indirectly proportional to the number of antennas in the 

array. Typically from 4 antennas to 8 antennas are used to achieve the desired 

beamwidth. The resulting increase in antenna gain increases the link margin in both the 

DL and UL directions and significantly reduces the potential for interference in UL. 
129 

Figure 55 - Beamforming 

Since all of the terminals must maintain a connection with the BS at all times, the range is 

determined by the control channels which are generally linked in a sector-wide broadcast 

mode. The added link budget in a beamforming system therefore does not translate to a 

significant increase in range or coverage but does enable a higher throughput and higher 

link availability. 

6.2.3 Practical Considerations and Spectrum Trade-offs 

Advanced antenna systems can definitely provide significant performance advantages for 

terrestrially-based wireless access systems, however one must also consider the 

deployment implications, especially in the lower frequency bands. The impact on 

spectrum choices with respect to multiple antenna systems was briefly mentioned in 

section 5.2.2.2.2. 

As described above, antenna spacing is an important consideration for these systems to be 

effective. This can be especially challenging for higher order MIMO systems in the 

lower frequency bands. The Figure 56 shows the spacing for a (4×4) MIMO array 

assuming, for illustrative purposes, an antenna to antenna spacing of three wavelengths. 

Note that this is a minimum requirement which will result in reduced performance due to 

the potential for antenna to antenna correlation as compared to a spacing of five or more 

wavelengths. 
130 

Figure 56 - (4×4) MIMO array with 3 wavelength spacing 

Only taking into account the antenna spacing, at frequencies below 1000 MHz the size of 

a (4×4) MIMO array will exceed 4 m with a 3-wavelength antenna separation. 

The dimensional requirements for beamforming arrays are somewhat better since the 

antennas are spaced at one-half wavelength apart rather than several wavelengths. 

Nevertheless, since beamforming arrays generally require more elements to be effective, 

the arrays still get quite large in the lower frequency bands as shown in Figure 57 for an 

8-element beamforming array. 

Figure 57 - An 8-element beamforming array 

In addition to antenna spacing the relative size of the antenna itself must also be 

considered. The gain of an antenna can be expressed as: 

Antenna Gain in dBi = 10 Log10 (4πηA/λ

2

), 
131 

where 

A = the size of the antenna aperture, 

η = the efficiency (generally a value between 30 % and 50 % depending on the antenna 

type design), and 

λ = wavelength. 

As the equation indicates the antenna gain varies inversely as the square of the 

wavelength thus providing a significant variation in gain for a fixed antenna aperture over 

the frequency range 700 MHz to 6000 MHz. 

BS antennas for wide area networks are typically designed to provide a fan-shaped 

pattern with a gain in the order of 12 dBi to 16 dBi. Figure 58 shows the gain for a BS 

antenna with an aperture to height ratio of 10 to 1 and an aperture height of 0.67 m. This 

provides a gain of about 15 dBi at 3000 MHz assuming an aperture efficiency of 50 %. 

Figure 58 - Antenna gain variation with frequency 

In Figure 59 the antenna aperture height required to maintain a 15 dBi gain over the 

700 MHz to 6000 MHz is plotted. This, of course, is a simplistic analysis since it does 

not consider alternative antenna designs that can potentially improve the aperture 

efficiency in the lower bands. Although the curves in Figure 58 and Figure 59 may 

present a more pessimistic prediction for the lower frequency bands the general trend is 

the same; antennas in the lower bands will be larger and have lower gain. 
132 

Figure 59 - Estimated aperture height for 15 dBi BS sector antenna 

When deploying MIMO or Beamforming arrays, taking into account the required antenna 

spacing and antenna size, one must carefully consider the following factors: 

• Visual impact 

• Space requirements on existing utility infrastructure (utility poles, transmission 

towers, etc) 

• Structural requirements for wind-loading forces. 

As described above the challenges with respect to these deployment factors are greater in 

the lower frequency bands. Since some of the commercially available advanced antenna 

systems may prove impractical in the lower frequency bands due to the antenna and array 

size, the path loss advantage is mitigated somewhat. Analysis will indicate that there is 

still a significant coverage advantage in the lower frequency bands, but the point to be 

made is that the projected path loss difference should not be the sole criteria for selecting 

these bands. 

6.3 Multi-Link / Multi-Hop / Mesh Topologies 

Several networking topologies were briefly discussed in section 4.2.2.4. This section will 

provide additional discussion of some of these topologies and specifically, those more 

commonly used in the Neighborhood Area Networks (NAN). One definition of a NAN is 

a common network infrastructure that links multiple intelligent devices in a relatively 

small or neighborhood sized geographic area. This section will provide a general 

overview of the NAN and several important considerations for its use in a Smart Grid 

environment. Many of the considerations described with respect to NANs also apply to 

mesh topologies in Wide Area Networks (WAN) and Field Area Networks (FAN). 

Figure 2 of section 3 describes the Smart Grid Conceptual Reference Diagram illustrating 

the multiple Domains and the network infrastructures interconnecting those domains. 

Shown are two holistic NAN infrastructures; one identified as a Field Area Network 
133 

(FAN), and the other identified as an AMI Network. Although the FAN and AMI could 

be the same network they are each shown as composite but separate networks in this 

diagram in order to show their relationship and support for two of the Smart Grid 

Reference Domains. More specifically, the FAN is depicted as supporting and serving 

the Distribution Domain, and the AMI Network supporting and servicing the Customer 

Domain. While this depiction is appropriate for the conceptual or high level Domain 

view of the SG, it is somewhat misleading from the physical network implementation 

perspective. In actual practice both the FAN and the AMI Networks can and usually are 

composed of multiple smaller geographically based networks or NANs, each with one or 

more Data Aggregation Points (DAPs) as will be described in greater detail below. Also 

depicted in Figure 2 of section 3, the communication network end-points for the AMI 

Network NANs include the two-way AMI Meters and ESI – Utility. The end-points for 

the FAN and NANs include distribution feeder devices and may also include a FAN 

gateway linking with a Substation Network. While the conceptual reference diagram 

illustrates the domain view of the SG systems, it should be noted that both the 

Distribution Domain and Customer Domain will have significant geographic overlap, and 

thus the network infrastructures serving them likewise will have significant geographic 

overlap. This overlap leads to the potential for integrating the FAN and AMI Network 

NANs in these common geographic areas into a common network infrastructure. Thus in 

these areas, a single composite NAN may be implemented to provide connectivity for the 

end-points from both the AMI Network (i.e., the AMI meters) and FAN end-point 

devices (i.e., field Distribution Automation devices). Throughout the rest of this section 

the distinction of the Domains served will be considered only insomuch as the end-point 

devices for the Domains will bring different use cases and thereby bring different 

network requirements. However, the primary focus of this section will be on the 

underlying NAN infrastructure and considerations for supporting the uses cases for the 

various end devices it serves. The subsections below will cover the components of the 

NAN in greater detail but a common network element of the NAN is the Data 

Aggregation Point (DAP), as mentioned above. While the different AMI and NAN 

vendors typically identify the DAP using their own specific product names, the primary 

purpose of the DAP is to serve as a gateway from the NAN to other networks to 

ultimately link back to one or more common application system and services. In some 

cases more sophisticated NANs allow more than one DAP for an individual NAN that 

may allow segregation of traffic types and may allow linking with different upstream 

application systems. Other commonalities and distinctions will be discussed further in 

the following subsections considering the technology and topology of the NAN. 

Furthermore, for the current Smart Grid implementations the dominate use of the NAN is 

for AMI. In these implementations the NAN provides connectivity between AMI meters 

and a DAP, with the DAP having a backhaul path via a WAN to the host AMI System or 

AMI Head-End. For these dedicated AMI system there are specific requirements for 

information flow between the back-office metering systems, through the NAN, and to the 

individual AMI meters. For these AMI systems, there is no need for the meters to share 

their metering information amongst themselves, but only to share this information with 

the AMI Head-End and the centralized metering system. Therefore meter to meter (i.e., 

peer-to-peer) information sharing in the NAN is not required. However, in a Multi-Hop 
134 

AMI NAN the meter communication modules may be called on to relay messages 

between the DAP and other meters too far removed from the DAP to directly link with it 

and they may also exchange network housekeeping messages between the 

communication modules of neighboring AMI meters. As these AMI NAN networks are 

expanded to include DA devices there may be additional requirements for the NAN to 

support direct peer-to-peer information exchanges between these DA devices leading to 

additional requirements for the NAN to support these peer-to-peer routes along with 

effectively managing message priorities. Both the current and potential future 

requirements of the NAN should be fully considered when choosing a technology and 

topology for NAN connectivity. 

6.3.1 Network Topology Revisited 

In order to better identify the common NAN infrastructure a brief digression further into 

network topology is in order. Multiple terms are often used in conjunction with the 

NAN; Point-to-Multipoint (PMP), Multi-Link, Multi-Hop, and Mesh. However, these 

terms may be misleading and it can be said may inaccurately describe the commonly 

deployed NAN topologies. 

The term, Multi-Link network, can be defined as interconnecting multiple discrete 

networks, such as linking a HAN with a NAN, then to a WAN. The obvious reason for 

interconnecting these networks is to allow data exchanges between the devices connected 

to these discrete networks. That is, a Multi-Link network path can be established through 

the linked HAN, NAN, and WAN Multi-Linked networks.

The term, Multi-Hop network, can be defined as group of interconnected nodes in a 

common network infrastructure where links to traverse this network can be established by 

using node-to-node or hop-to-hop links, thus the term Multi-Hop. 

The term, Mesh, is used to describe a family of interconnected nodes in a common 

network infrastructure. There are several forms of mesh topologies which are well 

documented in multiple books and other technical papers on network systems. However, 

briefly here, the term full mesh is commonly used to describe a mesh where each node is 

directly connected to each other node, which can lead to an inordinately large number of 

links in large networks but also provides the largest number of direct communication 

paths. With a larger set of links, a full mesh has more choices to dynamically adapt to 

faults or traffic loads in any given communication link. A partial mesh is a subset of a 

full mesh where not all nodes are directly linked to all other nodes, and traversing the 

network may involve relaying or routing through multiple nodes or in essence forming a 

Multi-Hop link. 

The goal of a classical mesh network is to provide connectivity from any node to any 

other node. However, in an AMI NAN, the application level or use case data is usually 

limited to exchanges between the DAP and the AMI meter. Stated another way, within 

the AMI NAN the community of interest is between the DAP and the individual AMI 

meters with one being considered the source and the other being considered the 

destination or sink of the data. Thus for an AMI NAN, the goal is to provide links from a 
135 

DAP to the AMI meters. A potential exception to this DAP to end device community of 

interest within a NAN may be if DA devices are linked via the NAN and the DA 

applications or use cases require some peer-to-peer data exchanges between the DA 

devices. Node-to-node data exchanges for AMI NANs are otherwise generally limited to 

network housekeeping messages where the AMI meter communication modules share 

information amongst themselves on link and network status and best routes. However, it 

should be obvious in Multi-hop AMI NANs the meter communication modules will often 

be used as relay points to relay messages between the DAP and other AMI meters further 

downstream. 

As indicated in section 4.2.2.4 the nodes in a Multi-Hop NAN are typically intelligent 

devices (end-points), that have the ability to discover (multi-hop) forwarding paths in the 

network and make their own forwarding decisions based on various pre-configured 

constraints and requirements. Using this dynamic routing capability, the end-points first 

determine which neighbor nodes are within radio range, assess potential links with these 

neighbors and dynamically choose the best or most appropriate path as their primary 

route through the NAN to an DAP. Depending on the routing algorithms implemented in 

the end-point nodes, this best route determination may be based on RF signal strength, 

ability to exchange messages with neighbor nodes with minimal errors, using neighbors 

that provide the minimum number of hops back to an DAP, the geographic coordinates of 

the nodes, or a combination of these. The subject of routing and route determination 

algorithms for wireless Multi-Hop networks has been the subject of study for many years 

and multiple books and other publications are available on this subject. Suffice it to say 

here that the meter nodes will use their programmed algorithms to select what they have 

determined as the best route back to a DAP at that particular point in time. It has been 

noted that some of the dynamic self-configuration algorithms implemented in currently 

deployed NAN networks may occasionally lead to unstable or otherwise infeasible routes 

and may need some external oversight to force a usable route or the additional of other 

NAN network devices to establish useful routes. 

In addition to selecting a primary path and route to a DAP, an AMI meter node will 

typically also preselect other (second best) routes to be used should its primary route 

become degraded or unavailable. Normally all AMI meter traffic to and from a DAP will 

use only their currently established primary path. The secondary path would be used if 

and when it is promoted to be the primary path because the previous primary path had 

degraded or become unserviceable. 

An interesting side note is that while the NAN AMI meter nodes have the ability to link 

with any of their neighboring nodes (within RF range) and potentially route through 

them, in practice as the routes are established between the DAP and the AMI meters, the 

routes usually form a tree network structure as shown in Figure 60. For an AMI NAN 

this is a consequence of the need and requirement that the AMI meters exchange 

information only with their selected DAP, using the Multi-Hop links through other AMI 

meter nodes merely as relay or routing points. In this tree structure the primary branches 

or links extend from the DAP to a first layer of nodes and from this first layer of nodes 

additional branches or links extend to secondary nodes, and so on until all nodes have 
136 

established a path between themselves and the DAP. The distribution of the number of 

hops from the end nodes to the DAP is then a general index of how effective the NAN 

may be in providing connectivity to the devices in that NAN. The nodes further removed 

from the DAP having a greater number of hops to traverse will also have greater latency 

and are generally more susceptible to having their path to the DAP interrupted as 

propagation conditions in the NAN may change. 

Figure 60 - Full mesh, partial mesh, and tree topologies 

6.3.2 The Neighborhood Area Network (NAN) in a Larger Context 

Another concept is the use of the term, AMI Network, to describe a single monolithic 

network spanning large geographic areas. However, an individual NAN infrastructure 

will consist of at least one DAP and a number of end-point nodes or meters linked to it, 

and potentially other relaying / routing devices to extend the reach of the NAN or to 

provide additional reliability. Thus the term, AMI Network, while generally used to 

denote a single monolithic network, is in reality an aggregation of multiple individual 

NANs, each consisting of a DAP and the end nodes connected to and through it. These 

multiple individual NANs collectively provide coverage and connectivity to the end 

nodes in larger geographic area. However, this is not to say that the individual NANs 

operate totally autonomously. 

For the example where the AMI NANs use mesh technologies, as several DAPs are 

deployed in a geographic area, the AMI meters (end-points) in this area typically use 

dynamic routing to establish the best route to a DAP, which in some cases may not be the 

geographically closest DAP. In reviewing the links established in working AMI NANs, 

it is often observed there are significant areas where individual meters in common 

geographical areas establish links to different DAPs. One of the obvious advantages to 

this is the potential redundancy offered in areas with multiple DAPs. If one DAP fails, or 

the backhaul link serving it fails, the meters normally served by that failed DAP typically 

will dynamically reroute to link with other meters connected to other working DAPs. An 

example of this is shown in Figure 61. 
137 

Figure 61 - Node reroute example – failed DAP 

In addition to rerouting to bypass a failed DAP the individual nodes have the ability to 

dynamically reroute within the NAN should the path currently chosen as their primary 

route to the DAP become unavailable or unreliable. In this case, the node will typically 

try to route through its preselected second best route, or if that also fails, the node will 

continue to evaluate neighbor links to find the best route back to an DAP. An example of 

this is shown in Figure 62. 

Figure 62 - Node reroute example – failed node 

Similarly if an intra-NAN link fails, the nodes that were supported through that failed 

link will reroute to establish a new path to a DAP. An example of this is shown in Figure 
138 

63. However, in this example note the new route chosen is to a different DAP only 

because that new route may have been determined as the best route to a DAP during the 

route recovery process. 

Figure 63 - Node reroute example – after failed link 

6.3.3 Other Neighbor Area Network (NAN) Topologies 

While the focus of this section is on the AMI Multi-Hop network topologies it is of value 

to mention other alternatives commonly used to provide connectivity between AMI 

meters and the AMI host or AMI Head-End system. 

One example is the use of commercial or private cellular-like networks to establish pointto-multipoint links from a master radio site or BS to the end-point devices. While 

commercial cellular networks are designed and deployed primarily to support mobile 

devices moving from cell to cell, they can and do also serve fixed devices like AMI 

meters. In these networks, each BS typically serves a limited geographic area and when 

covering large geographic areas, multiple BSs are deployed to provide overlapping 

coverage to help ensure ubiquitous coverage. Utilizing commercial cellular services for 

linking with AMI meters may be appropriate in areas where implementing private or 

purposely built wireless networks are not feasible or are not cost-effective. 

Other AMI systems may use purposely built point-to-multipoint wireless networks 

designed specifically to support fixed locations. As such they can be implemented 

without the additional complication of tracking and handing off mobile devices as they 

move from cell to cell. These are generally tower-based systems and are typically 

designed to provide connectivity extending multiple miles to remote AMI meters. A 

single master radio or BS therefore, may be able to provide coverage over a large 

geographic area. 
139 

In both point-to-multipoint networks described above, the master radio or BS would then 

be linked through an appropriate backhaul network to a centralized AMI system or host. 

Point-to-multipoint networks may be implemented to cover different geographic areas 

ranging from pico-cells covering tens to hundreds of feet across to mega-cells covering 

areas hundreds of miles across. Pico-cells might be appropriate in small geographic areas 

like meter closets in multiple dwelling units, whereas satellite-based mega-cells might be 

appropriate for linking multiple remote meters spread over large geographic areas 

covering thousands of square miles. Use of these technologies should be considered 

when the use of other primary technologies may not provide effective coverage for all 

meters in a service territory. These examples are illustrated in Figure 64. 

Figure 64 - Point-to-multipoint implementations 

6.3.4 NAN Network Components 

This section identifies some of the network elements commonly used in implementing 

NAN systems. One of the original purposes of the NAN was to provide network 

connectivity from a centralized metering system to remote AMI meters, thus the original 

name AMI NAN. Using the network connections, the centralized metering system was 

able to retrieve energy usage and other information from the AMI meters and to send 

configuration and other command and control functions to them. 

Typical AMI NAN implementations also included a centralized AMI Head-End 

management system linked to the NAN through a backhaul network to provide network 

management and control of the NAN elements themselves. This AMI Head-End system 

is depicted in network diagram Figure 2 of section 3. 

As for the NAN nodes themselves, to some extent the type and function of these nodes 

are dependent on the technology and topology used to implement the NAN. However, 

there are some elemental components commonly used in these nodes. For wireless 

NANs, one of the elemental components of a node is a NAN radio. The NAN radio is 

Example Implementations of Point to Multipoint Topologies

Tower Based

Cell

Satellite Based

Mega-Cell

DAP Based

Pico-Cell
140 

typically modularized and will consist of an RF transmitter and receiver, antenna, and the 

control electronics which allow the radio (and thus the node) to actively participate in the 

NAN. NAN radios are mated with end devices, for example, AMI meters, DA devices, 

etc., typically in a composite package provided by the Original Equipment Manufacturer 

(OEM) or end device manufacturer. For AMI meters, this package is almost always in 

the meter housing itself or under cover. In any case, the NAN radio provides a data 

interface used to interconnect with the meter or other end device. Other NAN radios may 

be implemented as standalone repeaters, relays, or routers used to strengthen and/or 

extend the reach of the NAN. Also common to wireless NAN systems are the DAPs. 

The DAP serves as the centralized connection point for its supported NAN nodes and 

thus will include one or more NAN radios to link with these nodes. The DAP will also 

contain a backhaul network interface and necessary controlling electronics to allow it to 

be linked with the NAN Head-End through a backhaul network. 

Depending on the vendor and NAN networking technology used, the NAN radio module 

may also be capable of supporting additional data processing functions as may be 

required to properly interface with its connected end device. This data processing 

capability may be necessary to aggregate end device data for more efficient network 

transmission through the NAN or to accommodate specific application level or native 

protocols utilized by that end device. 

6.3.5 Characteristics of NAN Multi-Hop Networks 

When employing a Multi-Hop network topology, there are additional network 

considerations that must be taken into account. These considerations include: 

• Mesh Multi-Hop Latency: Each application and use case may bring additional 

latency requirements. In a Multi-Hop NAN, latency can be significantly impacted 

by several factors including the node-to-node effective data throughput rate 

(which, in turn, is related to RF bandwidth, RF modulation efficiency, and error 

rates) and the internal processing delay introduced by intermediate nodes in 

relaying data packets. Consideration should be given to the distribution of the 

number of intervening nodes or hops that may exist between the DAP and nodes 

in that NAN. The cumulative or combined latency of multiple hops encountered 

in reaching end nodes further removed from the DAP may be significant. 

• Incremental expansion of the mesh topology: Both in number of devices and 

functionality without risk to the existing topology. 

• Resiliency and Redundancy: Requirements for recovery of failed network 

elements along with the techniques employed to provide redundancy 

6.3.6 Additional Technical Characteristics of the NAN 

Several vendors offer NAN systems designed to support AMI and other SG data 

requirements. While most of these employ the use of Mesh or Multi-Hop networks as 

described in this section, they can vary significantly with respect to their implementation. 
141 

Several technical issues to consider when selecting a technology, topology, and vendor 

include the following: 

• Spectrum Used – Many commercial AMI NAN systems use the 900 MHz 

(902 MHz to 928 MHz) ISM band. Others use the 2.4 GHz ISM bands. These 

bands are unlicensed, and with some technical restrictions on their use, they are 

shared by multiple users. Other systems use private licensed frequencies, thus 

minimizing potential interference with shared users. Still others may use the 

registered but non-exclusive 3.65 GHz band. Regardless of the spectrum used, 

the capabilities and limitations inherent with this spectrum should be considered 

in choosing a NAN technology and topology. A number of other sections 

provide much more information on the capabilities and restrictions of the 

spectrum choice. 

• Route Forming and Maintenance – As a NAN (i.e., a DAP and associated end 

devices) forms a PtP, PMP, mesh, or Multi-Hop network; a set of data paths or 

routes will be established from the end devices to the DAP. These routes will 

form as a result of the dynamic self-routing ability inherent in the nodes of the 

NAN. As indicated in section 4.2.2.4 the nodes in a Multi-Hop NAN are 

typically intelligent devices that have the ability to discover (multi-hop) 

forwarding paths in the network and make their own forwarding decisions based 

on various pre-configured constraints and requirements. 

Consideration should be given to the length of time it takes to initially form and 

stabilize the routes, how often and under what conditions automatic route 

maintenance (i.e., the process of analyzing network performance data and 

automatically performing node-to-node incremental network tuning) is 

performed, as well as routing recovery time for internal NAN node failures. 

Another important consideration is the ability of the NAN nodes to route to an 

alternate DAP should a node’s primary DAP fail. Yet another important 

consideration is the ability of a NAN to recover after a significant widespread 

power outage. The ability of the nodes to hold their current configuration and 

routes in non-volatile memory would offer a significant advantage for quickly 

restoring operation after power is restored, although some network churn may be 

expected as the nodes are repowered and begin to return to normal operation. 

Related to these automatic routine process is the possibility that due to the long 

lasting loss of end device nodes or any long lasting changes in the RF 

environment in an area may necessitate a manual redesign process including the 

relocation or addition of the DAPs, repeaters, relays, or routers. 

• NAN Throughput – This measures the capability to support the volume and 

latency requirements for data exchanges from the DAP to the end nodes. This is, 

in turn, related to multiple technical aspects and operational processes used by the 

DAP and other NAN nodes. The DAP must support the interconnection with the 
142 

WAN network, maintain the links with its internal NAN nodes, and process and 

relay all data to and from all of the DAP’s attached NAN nodes. The internal 

NAN nodes must relay data for its supported downstream nodes and also process 

all data exchanges between themselves and the DAP. Considerations should be 

given to the following technical aspects: 

o The processing power of the DAP 

o Number of discrete radios in a DAP 

o Processing power of the internal NAN nodes 

o The bandwidth of the NAN radio links 

o The RF modulation scheme used 

o The point-to-point latency 

o Data fidelity checks and recovery processes used in the NAN 

Equally important to these technical aspects is the degree to which the NAN 

technology is conforming or adhering to the standards being fostered and 

promulgated by the SGIP in the Catalog of Standards (CoS). For greater detail of 

the technical aspects, multiple books and technical papers have been written on 

these subjects. Suffice it to say here, these are important items to be fully 

considered in selecting a NAN infrastructure. 

6.3.7 Further Considerations for NAN Design and Routing 

This subsection addresses some of the complications that would be encountered in 

designing Multi-Hop NANs. 

As indicated in this section, a Multi-Hop NAN generally will be self-forming by the 

nodes themselves using dynamic routing algorithms. To use standard RF modeling 

techniques to predict the optimal routes that would be formed in a NAN and then predict 

the operation characteristics of the combined links and routes for each NAN node would 

be a formidable task. Consider that the number of nodes or AMI meters in a NAN and 

linked through a single DAP may be on the order of thousands, and as a result, there may 

tens of thousands of candidate RF links that would need to be modeled and evaluated to 

determine an optimal connectivity path or route from each internal NAN node to the 

DAP. Further complicating this exercise would be the recognition that a NAN will be in 

a dynamic RF environment. Considering that nodes are subject to being added or 

removed, obstructions such as vehicles changing locations, changing weather conditions, 

vegetation changes throughout the year, etc.; the NAN will always be in some state of 

flux as it dynamically adapts to the changing environment. To model this environment 

would be an extremely difficult task when using discrete RF modeling techniques as may 

be used in modeling point-to-multipoint network links as described in section 5. 

Considering the sheer number of point-to-point links possible in a Multi-Hop NAN 

network consisting of thousands of relatively closely located end nodes makes this an 

almost impossible task. In summary, each individual link has associated with it a specific 

probability, at any given time, a satisfactory connection will be achieved (i.e., having a 

received signal above threshold) it is a daunting task to come up with an easy-to-use 

mathematical model to analyze and accurately predict the routing within a NAN. 
143 

Even with the difficulties identified above, most vendors supplying AMI meters and/or 

the Multi-Hop networks to support them have typically developed a set of internal tools 

and capabilities to assist them in developing effective and efficient Multi-Hop NANs. 

Given the number and location of the AMI meters or other end-points to be covered, they 

use these internal tools along with knowledge of the terrain, clutter, and other 

characteristics for a particular geographic area, combined with rules-of-thumb they have 

developed while implementing previous NAN systems to produce the infrastructure 

designs. Using techniques and processes they have found to be successful allows them to 

predict with some degree of accuracy the number and placement of the DAPs and RF 

repeaters or relays or routers required to effectively service the specified number and 

location of the AMI meters or other end nodes. It is worth noting that these vendors may 

also have internal simulation tools to validate their designs prior to implementation. 

However, it would be prudent and indeed necessary to validate expected performance of 

the NAN after implementation in the field and to augment or adjust infrastructure if 

necessary to achieve required performance. 

Finally, there are evolving commercially available RF software modeling tools 

specifically designed for designing and analyzing Multi-Hop NANs. These tools use the 

proposed number and location of the AMI meters or end-point devices to be included in 

the NAN, along with some NAN design constraints (for example the maximum number 

of hops allowed from an end node to the DAP) to develop an infrastructure design. 

These tools take into account the combined RF coverage that would be provided by the 

proposed infrastructure devices (DAPs, relays, repeaters, and routers) along with the 

additional RF coverage that will be provided by the end nodes themselves to propose an 

infrastructure design which would include the predicted routes the AMI meters or other 

end nodes will form when linking back to the DAP. These tools allow a designer to 

specify design constraints like load and maximum hop counts allowed within the NAN. 

However, the routes formed within a NAN when deployed in the field will likely not 

exactly match the predicted configuration for the same reasons mentioned above relating 

to the dynamics of the RF environment in a given area. Nevertheless, the proposed 

infrastructure design should enable the formation of a Multi-Hop NAN suitable for 

providing effective and reliable connections of the NAN end nodes with the DAP. As 

one might expect, these tools are extremely process intensive and may require 

multiprocessor computing power to develop designs within a reasonable amount of time 

for large numbers of end devices covering large geographic areas. An example of several 

relatively small NAN designs and their predicted links or routes is shown in Figure 65. 
144 

Figure 65 - Example of NAN design from software design tool 

6.3.8 Smart Grid Neighborhood Area vs. Wide Area Networks

The preceding sections have provided considerable detail concerning Multi-Hop 

networks with a specific focus on the smart grid NAN networks utilized to provide 

connectivity to multiple end-points such as meters and/or DA devices. However, the 

Multi-Hop topology may be utilized in other Smart Grid network systems as well. More 

specifically Wide Area Networks (WANs) may be implemented utilizing PMP, Mesh, or 

Multi-Hop techniques and topology. This section briefly considers these WAN 

implementations in the context of the Smart Grid network. In general a NAN PMP, 

Multi-Hop, or mesh network may have a number of similarities with a WAN. However, 

due to differences in the intended uses, specific use cases, capacity, and reliability 

requirements for which these networks are designed to support, will often lead to 

significant differences in implementation technology and operational characteristics. A 

brief comparison of some general requirements and operational characteristics will help 

illustrate these differences as listed in Table 19.

 
145 

Table 19: Differences between NANs and WANs 

Requirement / Characteristic NAN WAN 

Geographic coverage Neighborhood area size –

ranging from several hundred 

square yards to a small 

number of square miles or 

square kilometers 

Larger geographic areas –

ranging from multiples of 

square miles up to hundreds 

of square miles or square 

kilometers – or even larger. 

Technology used Usually a single technology –

usually wireless with a 

common physical interface. 

Can be made up of multiple 

technologies (wireless and 

wire-line / fiber optics) with 

bridging devices or routers 

used to connect disparate 

technologies. 

Node-to-Node physical spans or 

links 

Usually established and 

changed dynamically as 

needed by the nodes 

Usually established statically 

and may or may not be 

dynamically changed by the 

nodes 

Ability to route data through the 

network 

Dynamic routes can be 

established by the connected 

nodes through the dynamic 

links and can automatically 

update as needed by network 

topology changes. 

Dynamic routes can be 

established by the connected 

nodes through the static links 

and can automatically update 

as the state of the nodes or 

static links change. 

Number of connected nodes Ranging from tens to 

multiple thousands of nodes 

per individual NAN 

Typically ranging 10 to 100 

nodes – except for very large 

WANs (i.e., the Internet). 

Capacity per node-to-node link Ranging from tens of kb/s to 

several hundred kb/s (e.g., 

IEEE 802.15.4g) 

Typically several Mb/s to 

several Gb/s 

Node-to-Node distance Typically several hundred 

feet to several thousand yards 

or meters 

Typically multiple miles to 

hundreds of miles or 

kilometers. 

Example networks AMI NAN encompassing 10 

to 20 city blocks in an urban 

or dense urban environment 

A composite network 

spanning a city, county, or 

state – or even a global 

network. 

Another significant difference in the NAN and WAN may be in the source and ownership 

of the equipment used to implement the network. A WAN may be provided by a network 

service provider as a virtual private network and will appear to the user or customer as a 

cloud that provides connectivity between nodes as required and as negotiated in the 

service contract with the network service provider. In that case the topology and 

technology of the physical network is less important than the service level agreement 

contracted by the customer. Alternatively a user may elect to construct and build a 

private network infrastructure. The user may plan and develop the network by using 

internal network design talent or relying on a service provider or other contract services 

to design and implement the network. In this case the user is free and indeed obligated to 

specify the topology and technology utilized to implement the network. For a private 
146 

WAN the owner can specify and design a PMP or Multi-Hop network or other topology 

to best suit their requirements and budgetary and operational constraints. 

Just like for a NAN, for a WAN Multi-Hop or mesh network, considerations and 

evaluation must be given to the intended use and operational characteristics of the WAN 

before committing to a specific topology and technology. This is particularly true for a 

private WAN where the cost to implement the WAN may be substantial and mistakes in 

choosing the proper technology and topology caused by not properly evaluating the 

current and potential future requirements can lead to an untenable situation that can result 

in costly redesign and rework. 

In contrast, for an SG NAN, the network infrastructure and the end nodes (i.e., meters) 

are commonly owned by the utility. A notable exception to this is if the utility subscribes 

to a service provider to provide discrete links or lines of service to each end node. 

However, in this case it is not a question of the use of a Multi-Hop, or even if it is a NAN 

or a WAN but instead the significant element of concern to the utility is the service level 

agreement contracted with the service provider. 

In most cases the utility contracts with a NAN system vendor to develop and provide a 

customer owned network consisting of the necessary NAN infrastructure and head end 

services to provide connectivity to meters and other end devices required by that utility. 

Another distinction between a WAN and a NAN is that a NAN almost by definition is 

much smaller and less costly on an individual NAN instantiation basis. Mistakes in 

evaluating the capacity requirements for a particular isolated NAN can usually be 

corrected with minimal cost by adding another Access Point or forcing traffic through an 

alternate takeout point. However, all the caveats mentioned earlier should be carefully 

considered when specifying the operational characteristics of the NAN system to serve an 

enterprise. It must be remembered that the enterprise NAN system will be composed of 

multiple individual NANs to provide the required enterprise coverage. Mistakes at the 

system or enterprise level can also be very costly to remedy or correct. 

Although this section describes NANs and WANs as separate entities, in practice a WAN 

may be used to provide connectivity for SG end devices if this connection mechanism is 

more effective than implementing a NAN to provide this connectivity. The intent of this 

section is to acknowledge that there are a number of similarities in Multi-Hop NANs and 

Multi-Hop WANs, but there are also a number of differences in the purposes of these 

networks and the operational characteristics of each. 

This concludes a brief coverage of the WAN Multi-Hop network. This description is not 

intended to be a comprehensive treatise on all aspects of network theory, design, or 

operations. It is merely to acknowledge that there are multiple network types and 

classifications and the choice of technology, topology, and whether to build or buy 

services is going to be highly dependent on the utility and their business and regulatory 

environment, requirements, and obligations. 
147 

Finally, and again, this section is not intended to be the definitive guide for a utility in 

selecting their SG network technology or topology. Further information is available in 

other sections in this report and information in much greater depth can be obtained from a 

number of books and technical papers devoted to the theory of network design. 

6.4 Addressing the Challenges with Multi-Tenant High Rise Buildings 

A major challenge faced by utilities in Dense Urban and Urban centers is establishing a 

reliable communications link between the Smart Meter and the HAN in high-rise, multitenant buildings. With the utilities’ infrastructure generally underground, meter banks 

are often located at ground level in weatherized enclosures or below ground in basement 

locations. To consider an all-indoor wireless solution for this application, one must take 

into account the excess path loss caused by successive floor penetrations. 

A number of indoor path loss models were described in section 5.2.1.2. Two of the 

models, ITU-R M.1225 and WINNER II, include a parameter for predicting floor-to-floor 

penetration losses. Although the two models diverge considerably with an increased 

number of floor penetrations, both predict a higher penetration loss for the first floor with 

a diminishing loss per floor for successive floors. For three floor penetrations the ITU 

model predicts 25 dB excess path loss at 1900 MHz and the WINNER II model predicts 

44 dB. Another field study cited in section 5.2.1.2 predicts about 35 dB for three floor 

penetrations, which coincidentally, is close to the average loss predicted by the two path 

loss models. 

Whichever model is used to project floor-to-floor penetration loss, it is clear that 

propagation paths that go beyond 4 or 5 levels will create a major deployment challenge 

for an all-indoor wireless meter to HAN connection. In addition to the high total path 

loss, EIRP limitations to comply with human safety exposure limits further reduces the 

range potential for an indoor wireless link. One potential deployment solution is to 

position relays every 2 to 4 floors. This approach might be suitable for buildings under 9 

or 10 stories but probably not cost-effective for taller buildings. 

6.4.1 An Alternative Wireless Approach 

An alternative deployment approach for a wireless solution that can be considered for this 

scenario is one in which a basement-located DAP communicates with an outdoor polemounted32 BS acting as a relay to connect to apartment HANs on higher floors as 

illustrated in Figure 66. In addition to reducing the total penetration loss compared to the 

all-indoor wireless solution, this approach can take advantage of higher gain antennas and 

higher EIRPs on the outdoor pole-mounted BS labeled as relay node in Figure 66. 

 

32 Pole is used generically to indicate any suitable existing mounting location, such as a street light, traffic 

light structure, or the side of a building across the street. 
148 

Figure 66 - Approach for basement to HAN connections in multi-story buildings 

The Relay Node serves multiple functions. It provides a PtP link to the basement-located 

DAP, a PMP link to the HANs in the multi-tenant building, and a backhaul link to a 

macro-cell in the NAN or WAN. 

To better understand the attributes for this approach, it is informative to look at the 

characteristics of each communications link individually. 

Basement-located AMI Network: This is a totally indoor PMP link probably 

comprising a single sector in a license-exempt band to aggregate the data traffic to and 

from the smart meters. The meters are typically tightly clustered and the distances to the 

DAP will be relatively short. The DAP can be strategically positioned for the most 

optimal favorable propagation path to the outdoor pole-mounted Relay Node. Where 

very high penetration losses are anticipated one can consider running a line to an outdoor 

mounted antenna. The cable losses will be far less than the basement to outdoor 

penetration loss thus greatly increasing the link margin. 

DAP to / from Relay Node: Both the DAP and the Relay Node would employ a high 

gain, narrow beamwidth antenna to provide a PtP connection between the two sites. 

Although the DAP EIRP, depending on how and where it is mounted, may still be limited 

for compliance to human safety exposure limits, the narrow beamwidth will help mitigate 

the potential for interference in a license-exempt band. Even with dedicated spectrum, 

the narrow beamwidth will help protect against interference from a similar deployment in 

the adjacent building or the next block. With careful antenna positioning and alignment 

at each end of the link and the relatively short path length, the 20 dB to 30 dB penetration 
149 

loss will be easily accommodated. With the lower EIRP at the DAP the link budget in 

the UL will be the dominant determinant for the link budget and link availability. 

Relay Node to / from HAN: At the Relay Node this would be a single sector PMP link 

in an unlicensed band consistent with the operating frequency of the HANs. Rather than 

being optimized for surface area coverage, the PMP antenna would be positioned to have 

wide elevation angle and relatively narrow azimuth consistent with the building height to 

width ratio. The link budget would have to take into account at least one external wall 

and in some cases multiple internal walls to access individual HANs. The relatively 

shallow angle of incidence for the upper floors in a very high building will result in a 

higher penetration loss than that encountered with the lower floors. As in the last case, 

the link budget in the UL will be the major determinant for the link performance due to 

the lower EIRP and lower antenna gain for the HAN.

Relay Node Backhaul to / from macro-cell: The backhaul connection for the Relay 

Node would employ a high gain fixed antenna that is aligned to optimize the link 

between it and the macro-cell BS which would typically be mounted on one of the 

adjacent building roof tops for maximum coverage. The macro-cell BS would employ a 

typical PMP sector antenna to provide a backhaul for several Relay Nodes or other picocells (DAPs) within its coverage area. Since the Relay Node would be mounted on a pole 

about 8 m to 10 m above ground, accessible only by trained personnel, it would be able to 

operate at a higher EIRP. The narrow antenna beamwidth would also help to mitigate the 

potential for interference to the relay node. For this link the DL and UL link budgets 

would be quite comparable due to the higher antenna gain at the relay node. 

A schematic view of this proposed solution is shown in Figure 67. As was stated in 

previous sections, when considering end-to-end payload latency requirements it is 

necessary to apportion the latency requirement on a per-link basis. 

Figure 67 - Schematic view of the network architecture 

6.4.2 Conclusion 

Implementation of the alternative to an all-indoor wireless solution described above 

depends on having access to a conveniently located structure for mounting the relay node 

equipment and associated antennas. The location must provide a good propagation path 

to the antenna for the basement-located DAP, as well as to the HANs on the highest 

floors. In some situations it may pay to take advantage of the roof mounted macro-cell 
150 

BS site. This site may have a better propagation path to HANs on the highest floors of 

the building while the Relay Node can be used for connectivity to the lower floors in the 

building. 

6.5 SG Framework and Wireless Modeling Tool 

The modeling framework discussed in section 5.2 was extended both on the input 

parameters and the outputs of a wireless model. This section describes this extended 

framework and a wireless modeling tool that has been developed to exercise the 

framework to provide more information to help assess wireless standards, representative 

technologies, and spectrum band usage specific to terrestrial wireless Smart Grid 

networks. The SG framework and wireless modeling tool is a merger of the 'SG 

Networks Deployment Modeling Framework' developed by the OpenSG Networks 

Working Group and the 'Wireless Modeling Engine' developed by SGIP PAP02. 

 http://collaborate.nist.gov/twiki-sggrid/bin/view/SmartGrid/PAP02Wireless

with a file name syntax of 

“SG_Framework_and_Wireless_Modeling_Tool_V0.xlsx”, where N represents 

the number of the version. 

o https://collaborate.nist.gov/twikisggrid/pub/SmartGrid/PAP02Wireless/SG_Framework_and_Wireless_Mo

deling_Tool_V0.xlsx 

OR 

 http://members.sgip.org/apps/org/workgroup/sgippap02wg/download.php/1609/2013-09-17_sgippap02wg_00014_SG_Framework_and_Wireless_Modeling_Tool_V0.xlsx 

The SG framework and wireless modeling tool (see Figure 68), is structured to provide 

an estimate for the number of BSs33 required to provide ubiquitous coverage and the 

required BS to BS spacing to meet data throughput, payload latency, and reliability 

requirements called for by the demographics for the specific geographical area under 

consideration. 

 

33 The term, base station, as used in the context of the modeling tool, describes an aggregation point for a 

point-to-multipoint topology. Other terminology may be encountered with different land-based wireless

technologies to describe similar functionality e.g., Central Station, AP, Cell-Site, and specifically for SG 

Network Requirements: for AMI networks, Data Aggregation Point (DAP); for Field Area Networks, FAN 

Gateway. 
151 

Figure 68 – SG framework and wireless modeling tool

The smart grid requirements section of the tool accommodates several approaches for 

adaptation of SG requirements data for input into the SG Wireless Modeling Engine. 

Normally the utility or other organization wanting to use this tool would base the inputs 

on their specific SG Deployment area specifics. The tool framework also allows use of 

data proxies in the absence of specific SG Deployment details. The details of preparation 

and use of proxied input data as based on the SG-Network TF’s Requirements data is 

described in section 6.5.1. 

The wireless section of the tool utilizes five large scale terrestrial path loss models, the 

latency model, and other relationships developed and discussed in section 5 for outdoorlocated BSs. Whereas the modeling tool assumes outdoor-located BSs, terminal or endpoint locations can be specified as being indoors or outdoors. Recognizing the fact that 

no mathematical model is an ideal choice for all deployment scenarios, the modeling tool 

makes use of the five terrestrial models where appropriate. That said, depending on the 

range of requirement parameters that are inputted to the model, there will be cases for 

which multiple solutions result. It is left to the user to decide which is most appropriate 

for the specific case being analyzed considering the terrain characteristics and 

demographics for the region being analyzed. 

6.5.1 Modeling tool input parameters 

As shown in Figure 68, required inputs to the modeling tool fall into three categories: 

1. Deployment Area Demographic Requirements: This information would 

generally originate from the utility specific to the SG deployment area of interest. 

Alternatively readily available census data combined with local utility 

information can be used as a reasonable proxy. End-point actors quantities (or 

SG Wireless 

Modeling 

Engine

Proxy for 

Model Area 

Demographics

based on US 

Census Data

• House Hold

Units

• Area

Wireless Technology

Parameters

• Frequency

• Channel BW 

• System Gain

• Mod-Coding Scheme 

• Overhead Factors

Output 

• BS to BS Spacing

• Number of BS

• Actors per BS

• Goodput per BS

Proxy for 

Specific Utility 

SG Deployment 

• Actor 

Quantities

• Use Cases

• Payloads

Specific Utility 

Deployment Area 

Demographics

• Quantity & Type

of Actors

• Area 

Characteristics

Specific Utility SG 

Deployment

• Use Cases

• Payloads 

• non-functional 

(volumetric) 

requirements 

based on SG 

Network Rqmts

Scale, Specify 

Variables, 

Aggregate SG 

Network Rqmts

per model run 

needs

• Baseload and

Highload

• MB/s per area

• Model area size

• Model area

characteristics

• Payload latency

and reliability

• Antenna 

heights

• Net Spectral Efficiency
152 

densities), combined with substation and feeder circuit information can be 

converted into a data density requirement over the specific geographical area 

under consideration. The model supports inputs in square miles, consistent with 

US census data information, or km2

 for those preferring the metric system. 

Another input in this category is the terrain and area characteristics. 

2. Smart Grid Deployment Requirements: These inputs are specific to the Smart 

Grid use cases, payloads, and actors for the specific deployment profiles being 

studied. These inputs also include the payload volumetric (architectural nonfunctional) requirements. The OpenSG SG-Network Task Force System 

Requirements Specification [5] describes a methodology for adapting the SGNetwork TF Requirements Table data for input into the SG framework and 

wireless modeling tool. The SG framework and wireless modeling tool 

spreadsheet includes a tab that contains these same detailed steps. The following 

provides an overview of these steps. 

• Adaptation of SG-Network TF’s Requirements Table Data for Use in Network 

Modeling Tools

General Steps - Regardless of study analysis intent: 

Step 1) Select the Study / Analysis Deployment Profile (including end-points, use 

cases, payload requirement sets. 

Step 2)Identify which business application payload requirement sets (parent rows 

and selected comm-paths) are in play based on selections and restrictions from 

step 1 above. 

a) Flag the Deployment Profile parent rows 

b) Flag the Deployment Profile child rows, if any associated with the parent 

rows selected in Step 2) a) 

c) Optionally, identify the child's parent Rqmt Ref (used for back reference 

and audit purposes) 

d) Extract the Deployment Profile Requirements to a separate workspace 

Step 3) Select one value for the documented non-functional metrics where ranges 

or unspecified parameters (variables) are identified in the Requirements tab, 

specific to your business requirements. Optionally, modify the other fixed / 

specified metrics to your business requirements. 

a) How Often 

b) Business App Payload Latency 

c) App Payload Size 

d) Daily Clock Period Factor for Specific Hour 

Step 4) Scale the non-functional app payload metrics in the Requirements tab 

specific to the study / analysis deployment characteristics. This step is where, 

optionally, the type and quantities of the actors can use the census and modelarea parameters and relationships between some of the deployment 
153 

characteristics can be used as proxies for the specific utility deployment study 

/ analysis area actor quantities. 

a) Multiple actor payloads multiplier for shared child row data flows / 

interfaces 

b) How Often Actor Quantity conditional / qualified and root actor names 

c) Summarized Table of Actor Quantities 

d) Payload Frequency metric per unit of time per How Often conditional / 

qualified Actor 

e) Conditional / Qualified Actor Quantities 

Additional Steps for:

General Telecomm Traffic Modeling 

Step 5) Specify which application payload data flow and/or interfaces are to be 

studied / analyzed. 

Step 6) Specify the wireless uplink and downlink designation for the requirement 

rows. 

Step 7) Specify the use case payloads requirement as being baseload or highload 

traffic and Specify the associated payload frequency: 

a) tag the requirements as baseload and/or highload 

b) specify those qualified actor where their quantities vary from baseload to 

highload 

c) create baseload and highload payload frequency metric calculations for 

each payload as appropriate 

Step 8) Specify the application payload and application packet size and latency 

values: 

a) Specify a variable for telecomm application packet size (e.g., the payload 

portion of a transmitted packet that also includes protocol overheads) 

b) Calculate the number of packets to accommodate the application payload 

c) Adjust the payload latency metric to account for the adjustment made in 

step 6 for wireless technologies that require the use of a DAP or BS for 

end-point to end-point communication versus peer-to-peer 

d) Calculate the packet latency for each requirement row 

SGIP PAP02 Wireless Modeling 

Step 9) Seed the DAP quantities and refresh after initial model runs. 

Step 10) Specifying the calculations – Part 1 of the Non-functional 

application payload requirement metrics. Note: these are the raw calculations 

by each requirement row that are then further processed in step 11 to create 

the inputs into the SG framework and wireless modeling tool. For the various 

combinations of uplink / downlink and baseload / highload calculate the 

following: 
154 

a) Payload and packet rates (quantity per second) 

b) (MB/s) / mi2

c) Payload and packet size 

d) Payload and packet latency 

Step 11) Specifying the calculations – Part 2 of the non-functional 

application payload requirement metrics and consolidating for input into the 

wireless model. The SG framework and wireless modeling tool inputs from 

the SG-Network Task Force Requirements are categorized as follows for each 

modeling area density category (e.g., high density urban, urban, suburban, 

rural, low density rural): 

• RF Propagation Path Loss - Calculating the number of DAPs required to 

provide coverage for the data volume across the geographic area that 

contain the end-points: 

o (MB/s) per square-mile [(baseload or highload) and (uplink or 

downlink) traffic] 

o Study / analysis area (square mile) 

o number of end-points in the study / analysis area 

• Payload Latency Rqmts - Calculating the number of end-points that a 

DAP can support at a specific probability of satisfying the latency 

requirements [(baseload or highload) and (uplink or downlink) traffic]: 

o Message Rate # per second (RMSG) 

o Average time between Message in seconds (TMSG = 1/RMSG) 

o Average application packet (without overheads) size in bytes 

(PAVG) 

o Single Network Link Latency seconds (L) from [average or manual 

input or minimum] application packet latency 

o Probability that message event falls within latency window (PMSG

= L/TMSG) from [average or manual input or minimum] application 

packet latency 

3. Wireless Technology Parameters: Required information about the specific 

wireless technology under consideration will not only be technology-specific but 

in most cases will also be vendor-specific. System gain information for both UL 

and DL transmission is essential for estimating range and coverage. Channel 

bandwidth, modulation and coding schemes, and channel overhead factors are 

required to provide an estimate for the average channel or sector and BS goodput 

and net spectral efficiency. If the overhead factors available do not account for all 

higher level protocols, headers, encryption, etc., there is provision for the user to 

add to the OH to take these factors into account. In assessing different 

technologies in different frequency bands (and possibly different countries) local 

regulatory rules must also be considered. These rules will generally place 
155 

limitations on antenna gain and EIRP, two parameters essential for calculating the 

system gain, and may also impose restrictions on occupied spectrum or allowable 

channel BW. To facilitate the comparative assessment of different wireless 

access solutions, there is provision for 16 sets of wireless parameters, 14 of which 

are preloaded with information provided in the Wireless Capabilities Matrix 

described in section 4. 

6.5.2 Modeling tool outputs 

The modeling tool provides an estimate for the BS to BS spacing necessary to meet the 

data density and latency requirements while, at the same time, achieving ubiquitous 

coverage over the specified geographic area. In some cases, based on frequency, antenna 

heights, and region type, there may be more than one applicable path loss model thus 

resulting in two or more output results that may or may not be similar. When results 

differ, it is left to the user, based on more specific knowledge of the terrain 

characteristics, to decide on which result to use. Alternatively, one could simply rely on 

the more conservative result, which for planning purposes may be adequate. 

Figure 69 illustrates how the output information would apply to a specific area that is 

being studied. Since the BS requirements are rounded up to the next highest whole 

number, the combined BS coverage will meet or exceed the area coverage requirements. 

The tool also takes into account the fact that end-points at the cell edge in a multi-cellular 

deployment will generally have connectivity access to more than one BS as shown in the 

figure. This reduces the fade margin requirement thus enhancing the link budget and the 

effective BS range. 
156 

Figure 69 - Modeling engine assumes a uniform (hexagonal) BS to BS spacing 

6.5.3 Limitations of the modeling tool 

The modeling tool has been developed specifically to facilitate terrestrially-based 

wireless network planning with outdoor-installed BSs and terminals located indoors or 

outdoors in a Point-to-Multipoint (PMP) cellular-like architecture. The tool is not 

suitable for analyzing a mesh topology, which by its nature has the potential to provide 

coverage beyond that predicted for the BS itself. As currently configured the tool is also 

not intended to address indoor Home Area Networks (HANs). Although a similar 

approach can be used for HANs, additional study would be required to arrive at better 

mathematically-based path loss models for wall and floor penetrations, covering the 

frequency range of interest for residential and enterprise environments. The current tool 

is also not intended for analyzing satellite networks, as these would require a different set 

of path loss models. 

To estimate propagation path loss, the modeling tool is based on large-scale mathematical 

models, each derived from field measurements in selected environments as described in 

section 5. These models are convenient and easy to apply but have limitations as there is 

no practical way to develop a mathematical model that would cover all possible 

deployment scenarios over the unlimited range of terrain characteristics and building 

densities that are likely to be encountered over a large geographical area. 

The latency model that is embedded in the modeling tool is based on the binomial 

distribution methodology described in section 5.2.7. This approach provides a more 
157 

conservative estimate for supportable end-points than the M/M/1 or M/D/1 approaches, 

also described in section 5.2.7. The latency model assumes the average packet time for 

the end-points of interest is less than the required latency. This is a reasonable 

assumption for most Smart Grid network segments with channel data throughputs that are 

likely to be encountered with any of the wireless technologies being considered. An error 

message will indicate when the desired latency requirement cannot be met and there is 

provision in those cases to select a different latency, labeled as ‘Acceptable’, so as to 

determine the latency that can be met. For the latency predictions, the modeling tool also 

assumes all packets have equal priority. QoS, which provides a means for prioritizing 

latency-critical packets, is not taken into account. Therefore, in assessing the results with 

respect to latency for a specific wireless technology, it is important to also consider the 

QoS that is supported by the technology of interest. 

To estimate average channel data density and average channel throughput, the modeling 

tool assumes a uniform distribution of terminals (actors) over the area of interest. This 

may be a reasonable assumption in many suburban and most urban and dense urban areas 

where BS coverage areas are relatively small and households are close to being 

distributed uniformly over the coverage area of interest. It may not be an accurate 

assumption, however, in rural areas where clusters of closely spaced housing units can be 

separated by several miles or kilometers from other clusters with scattered individual 

housing units in between thus resulting in a very non-uniform distribution of terminal 

locations as illustrated in Figure 70. 

Figure 70 - Typical rural area demographics 

Typically in this type of environment, BSs would be deployed close to or within areas 

where housing or end-point clusters are concentrated, or where placement is needed to 

satisfy the application latency requirements. For this deployment scenario, channel or BS 

capacity will be under-estimated rather than over-estimated since a higher percentage of 

terminals will be closer to the BS than what would be predicted assuming a uniform 

distribution of housing units. It can also be argued that, for most land-based wireless 

technologies under consideration, the most important metric for rural area deployments 

will be range capability whereas average channel capacity will only be of secondary 
158 

interest. Average channel and BS throughput will play a larger role in the more heavily 

populated urban and dense urban areas. In these high density environments, it is quite 

reasonable, for planning purposes, to assume utility customers are uniformly distributed 

over the area of interest. 

The SG framework and wireless modeling tool can be an effective tool for comparing the 

relative performance of different terrestrial wireless technologies and to provide initial 

estimates of network BS requirements to meet coverage, data density, and latency 

requirements in a wide range of deployment venues. Since the tool does have limitations, 

it is strongly recommended, before actual deployments are undertaken that the results be 

supplemented with more detailed network planning and, in some cases, on-site field 

testing or RF surveying. This is especially important for extreme terrain characteristics 

and unique deployment situations as might be encountered with below grade, indoor, or 

vault-located smart meters in an AMI network. 

6.6 Interoperating and Interworking with Other Wireless Technologies 

Section 5 and most of what has been presented in section 6 so far has been focused on 

terrestrial-based wireless technologies in the 700 MHz to 6000 MHz frequency range. 

Although these technologies, which are all cellular-like, can and will play a significant 

role in any of the demographic areas of interest for Smart Grid networks, it must be 

recognized that they may not provide an optimum solution for all possible scenarios that 

are likely to be encountered. In this section we discuss some of the other technologies 

that are included in the Wireless Characteristics Matrix discussed in section 4 and, 

additionally where these different technologies should be considered for application in 

the Smart Grid communication network. 

6.6.1 Satellite Communication Networks 

Satellite communications is another wireless solution that can play a vital role in a Smart 

Grid network. It was already pointed out in section 6.1 how a satellite link could be used 

to provide or augment a backhaul connection in a rural Smart Grid network. In other 

rural or low density rural regions with extreme terrain characteristics, a satellite solution 

may prove to be the only cost-effective approach to reach all of the desired utility endpoints. The intent of this subsection is to provide additional insights as to the attributes 

and trade-offs for satellite-based networks. 

Satellite communications technologies have features which can be used for many of the 

use cases identified for the Smart Grid. For example, satellite services are available 

throughout North America and cover 100 % of the conterminous United States. This 

means that the same user terminal can work anywhere in any rural or urban location. 

Furthermore, since satellite communications is independent of any local infrastructure it 

is ideal for emergency response and restoration, as well as for a redundant path to support 

highly reliable communications. 

Satellite communications systems all operate in licensed-band spectrum. Mobile Satellite 

Services (MSS) spectrum is available in L-band and S-band and includes both 
159 

Geostationary Earth Orbit (GEO) constellations, as well as Low Earth Orbit 

constellations. Fixed Satellite Services (FSS) spectrum includes C-Band, Ku-Band, and 

Ka-Band. Fixed satellites are generally in the GEO. FSS is becoming a misnomer as 

portable, transportable and fully mobile terminals are routinely supported. 

The one-way path delay between a user terminal and a GEO satellite is between 117 ms 

and 135 ms. The round trip latency due to propagation between a remote terminal and a 

gateway hub station is between 468 ms and 540 ms. In addition to propagation there are 

processing and queuing delays which are dependent on the specific implementation. This 

delay is acceptable for most Smart Grid applications. While some latency is tolerable, 

certain events have to be logged with an accurate time stamp. The mobile satellite 

technologies are completely integrated with the GPS system for routine functions like 

spot beam selection and paging area location and many terminals are required to have 

GPS receivers. The fixed satellite technologies are not required to have a GPS receiver 

but are routinely integrated with GPS depending on the application. Therefore, accurate 

GPS time is available for a time stamp. 

The ubiquitous capability of satellite communications gives repair crews the opportunity 

to use satellite terminals or handsets anywhere in the United States where they may be 

dispatched in the event of emergencies. Mobile terminals in both MSS and FSS bands 

can be used at speeds up to 1,200 km/h with Doppler compensation but are more 

routinely used below 160 km/h (100 miles per hour).

In order to close the link and provide adequate margin, MSS satellites all deploy on the 

order of hundreds of spot beams throughout their coverage areas. Not only do spot 

beams provide improved satellite EIRP and Gain to System Noise Temperature ratio 

(G/T) but also increased capacity with frequency reuse. New fixed satellites feature 

similar numbers of spot beams. 

Several MSS terminal types are small handheld devices similar to a cell phone having 

low antenna directivity. These are ideal for emergency crew dispatch. Both data and 

voice are supported, though the available bandwidths are commensurate with the antenna 

gain performance and terminal type. 

Satellite communications typically rely on line of sight propagation. Fading from foliage 

and imperfect terminal orientation is tolerated in low directivity handheld terminals used 

in MSS. Some MSS terminals have several dB of directivity and operate best when 

oriented properly and may really be considered transportable. These terminals can 

support up to 590 kb/s of data in the forward direction or downlink direction from 

satellite to terminal and 186 kb/s in the return or uplink direction from the terminal to the 

satellite. These data rates exceed the required rates. 

Many MSS networks employ the 3GPP Iu-interface between the radio access stratum and 

the core network non-access stratum. The physical and media access control layers are 

optimized for the satellite radio propagation characteristics but the higher layers are 

integrated. MSS terminals can be dual-mode satellite / terrestrial and have a single 

shared protocol stack above the radio resource control layer so that mobility management 
160 

and session management are the same for both modes. Core networks can also be fully 

integrated sharing the same 3GPP Serving GPRS Support Node (SGSN). 

FSS terminals use highly directive offset parabolic dish antennas, between 0.5 m and 1 m 

in diameter. They support data rates up to 440 Mb/s in the forward link and 16 Mb/s in 

the return link. These terminals are intended for fixed transportable and mobile 

applications. 

6.6.2 Smart Grid Solutions in the 450 MHz to 470 MHz IG Band 

Relevant regulatory information for the IG Band (Industrial / Business Band) can be 

found at [34]. Note that the 450 MHz to 470 MHz band is a sub-band of the entire IG 

Band which, in addition, includes several other blocks of spectrum between 150 MHz 

and 512 MHz.

6.6.2.1 Frequency Usage and Capabilities 

This band is one of the oldest bands for industrial group use. It requires FCC licensing 

and usually requires a Frequency Coordinator34 when obtaining licenses in this band. 

When licensed, operators in this band are, in effect, operating a private network and 

generally have protection against interference and less restrictive operating conditions 

compared to operating in any of the unlicensed ISM bands. Compared to higher 

frequencies, this band is notable for its longer communication range, typically 5 km to 7 

km with an outside end-point antenna at a 1.5 m elevation, level ground, and a transmit 

power of 2 watts35 (33 dBm) [35]. There is also lower penetration loss for end-points in 

buildings or other highly obstructed areas. For in-building communications, lower 

transmit powers can be used. Transmitters generally use some form of Frequency Shift 

Keying (FSK) transmission and AM and FM usage is permitted. The channel bandwidth 

(as of January 1, 2013 in this band) is now all narrowband (± 12.5 kHz) around channel 

center frequency. Licenses are typically good for a 32 km radius from the BS although 

with appropriate licensing it is possible to string additional licenses on the same channel 

to daisy chain the use of the channel over greater areas. Due to the popularity of this 

band, spectrum availability in large metropolitan areas such as New York city may be 

limited. Simplex or half duplex communication is the usual method of radio to radio 

communication although limited paired channels are available for full duplex operations. 

This means that small short messages (less than a TwitterTM message) are the preferred 

messaging strategy in this band. These frequencies are particularly useful both in NAN 

and WAN designs. To prevent congestion in a given area of dense coverage such as an 

urban environment reducing transmitting power to limit range and using frequency 

diversity to prevent saturation and message collision issues are common design elements 

of these NAN’s and WAN’s. This band is particularly suited to longer distance and 

 

34 Frequency Coordinators are FCC certified private organizations that recommend specific channel 

frequencies most appropriate for the applicant.

35 Human safety exposure limits are not applicable at these lower frequencies. 
161 

sparse transceiver network trees. So it would be an ideal candidate for rural and suburban 

applications. Commercial and Industrial (C&I) and irrigation type monitoring and 

control applications where there may be increased distances between complexes or fields 

are also good candidates for this frequency band. 

There are approximately 2,180 individual channels in this part of the IG Band available 

for licensing. While these channels are generally available nationwide, it is unlikely that 

any given licensee would have universal nationwide access to any given channel in this 

band. This means Smart Grid applications must be able to handle multiple channels in 

their data backhaul designs when using this frequency band. 

6.6.2.2 Implementation Using the IG Band 

The Hata-Okumura Model can be used to evaluate coverage potential in this band (see 

section 5.2.1.3.1). This model generally assumes that roof top antennas are being used so 

this model should be used as a maximum or ideal usage model. For rural areas the ITU-R 

M2135-1 path loss model is also valid in the 450 MHz band. This model also requires 

BS antennas higher than neighboring roof tops but has entries for actual building heights 

and road-widths (see section 5.2.1.3.4). Use inside of buildings, even with the greater 

penetration capability of this frequency band, will limit a given transmitter’s effective 

range. This band is very useful in retrieving meter items such as pulse counts 

(odometers) data and is used in real time demand response and time of use applications 

where the age of the data is a critical component for implementing reductions or increases 

in energy or water usage in response to outside triggers or predetermined usage or pricing 

points. Usually a Smart Grid application will use a partial mesh network with a tree and 

multiple DAPs design. Repeaters can be used in difficult communication areas to assure 

that messages are delivered in a timely manner to the BS or DAP. Because of the risk of 

having messages continually passed back and forth between mesh elements in a message 

hopping technique, most systems have a limit to the number of hops a message can make 

before it is discarded. This limitation will define the maximum depth of a given tree. So 

as not to lose data most end-points will have a data-logger or equivalent device from 

which to independently recover missing data points when the RF and network conditions 

permit retransmission of the missing data. 

As important as the transmitting power is to these solutions, the receiver sensitivity is 

also an important factor when selecting a transceiver as the increased distances capable in 

this band are maximized with a high receiver sensitivity. Current models will typically 

detect signal strengths as low as -123 dBm to -118 dBm, or lower depending on the RF 

Baud rate. 

Antenna tuning, placement, and gain are also key factors in the success of 

communications networks in this band [36]. Typically the mobile units (or end-points) 

will use omnidirectional antennas. BSs (DAPs) depending on terrain may use beam 

antennas in their solutions, but the transmission signal range must remain within the 

licensed area so as to minimize interference issues and conform to FCC requirements. 

Maximum antenna heights are also a part of the FCC license. (Typically 12.2 m above 

the structure to which it is mounted) 
162 

6.6.2.3 IG Band Summary 

Wireless solutions for Smart Grid in the 450 MHz to 470 MHz IG Band offer the 

potential for increased range and coverage and reduced penetration loss for indoor 

located end-points. Additionally, since it is licensed spectrum, there is protection against 

inter-operator interference throughout the geographical area covered by the license and 

less restrictive operating rules as compared to wireless solutions in the unlicensed ISM 

bands. 

When considering solutions in the IG band, however, one must also take into account the 

trade-offs imposed by the FCC-mandated channel BW limitations. As stated above, high 

density regions with high data density requirements will require a pico- or micro-cellular 

deployment topology to compensate for the limited channel capacity and in the lower 

density demographic regions cell edge data rate performance requirements will play a big 

role and may very likely mitigate much of the range benefit. Nevertheless, with careful 

RF planning and frequency reuse, it is in rural and low density rural regions where the IG 

Band is likely to have the greatest benefit in the Smart Grid network. 

6.7 Assessment of Modeling Tool Results 

In this section the SG framework and wireless modeling tool described in section 6.5 is 

applied to various terrestrial cellular-like wireless deployment scenarios. This analysis 

will provide some insights as to the wireless BS equipment required to meet specific 

network requirements under varied terrain and demographic characteristics and wireless 

technology choices. The focus of this analysis is on the AMI / FAN or NAN with the key 

output being the number of DAPs required for deployment to meet the Smart Grid 

network coverage, capacity, and latency requirements. 

As described in section 6.1, a wireless deployment can be described as being either 

range-limited, capacity-limited, or limited in its ability to meet latency requirements. 

From a wireless technology perspective, whether a deployment will be range-limited or 

limited by capacity or the ability to meet latency requirements depends on the following 

key metrics: 

• Wireless channel goodput which in turn is a function of the channel BW, the 

modulation and coding scheme (MCS), and the total channel OH (including OH 

contributions from lower and higher layers). The modeling tool estimates the 

channel goodput using the methodology described in section 5.2.3. 

• Wireless range which is a function of the system gain, link margins for fading, 

interference, and penetration losses, and the path loss predicted by one or more of 

the wide area outdoor path loss models described in section 5. 

• Packet size and whether it is fixed or variable: As we will see later, this can have 

a major impact on the network’s ability to meet latency requirements and, to 

some degree, can also influence goodput and range. 

From an SG network perspective, key metrics are: 

• Density of Actors (or End-Points) 
163 

• Data payload requirements for baseload and highload in both the DL and UL 

direction 

• Average and maximum payload size in bytes 

• End-to-end latency requirement per payload 

In the SG framework and wireless modeling tool average data capacity requirements, 

expressed as a data density in bytes per square mile, are provided for five (5) 

demographic regions as shown in Table 20. 

Table 20: Density requirements for five demographic regions 

Dense 

Urban Urban Suburban Rural Low Density Rural 

Average Housing Units 

per square mile 

7483 1794 303 26 2.2

Average Commercial & 

Industrial per square mile 

1320 317 54 4.6 0.4

Average End-Points/mile214212 3447 1111 65 4

Average Baseload Requirements

UL bytes/mile21234 449 5239 10.4 1.4

Average UL Payload 

(bytes) 

1020 344 189 274 190

DL bytes/mile25.2 102 124 3.3 0.7

Average DL Payload 

(bytes) 

90 99 89 99 100

Average Highload Requirements 

UL bytes/mile216129 2314 51283 66 25.7

Average UL Payload 

(bytes) 

8116 1517 911 1538 3178

DL bytes/mile229327 5472 116023 108 9

Average DL Payload 

(bytes) 

65649 4806 2538 3003 1189

From a wireless technology perspective, both channel goodput and packet size will 

influence how well latency requirements are met for a given number of end-points. The 

effect that packet size has on performance with respect to range, capacity, and meeting 

latency requirements deserves further discussion. 

6.7.1 Impact of Packet Size 

The submissions made to fill out the Wireless Capabilities Matrix in section 4 indicate 

maximum packet sizes that vary from 96 bytes to 14,400 bytes for the UL and from 640 

bytes to 14,400 bytes in the DL. Channel BWs range from 0.208 MHz (208 kHz) to 

20 MHz in the frequency bands between 700 MHz and 5800 MHz and for the 450 MHz 

band, the submitted channel BW is 0.0125 MHz (12.5 kHz) 

The relative trade-offs between small and large packet sizes are summarized in Table 21, 

Table 22, and Table 23.
164 

Table 21: Small packet size trade-offs 

Pros Cons 

• A higher probability of falling within 

the latency window 

• Reduced requirements for cell edge 

goodput 

• Higher Channel OH since each packet 

must contain some OH bits 

• Payloads larger than the maximum 

packet size must be divided into 

smaller packets and the payload 

latency requirement must then be 

divided by the number of packets to 

arrive at a latency requirement per 

packet. 

Table 22: Large packet size trade-offs 

Pros Cons

• Better OH efficiency. 

• A greater number of payloads can be 

accommodated without breaking it into 

smaller packets 

• Very large payloads can be transmitted 

with less segmentation 

• Lower probability of falling within the 

latency ‘window’ 

• Larger packet size places greater 

demands on cell edge performance 

which in turn will impact the receive 

sensitivity at the cell edge 

• Higher probability of a packet error 

due to a bit error 

• For some combinations of channel bit 

rate and packet size, the packet time 

will exceed the packet latency 

requirement 

Table 23: Summary of end-to-end packet payload minimum latency requirements 

for a Suburban Region 

Maximum UL Packet Sizea96 bytes 2042 bytes 14400 bytes 

Baseload UL 0.080 sb1.33 s 2.40 s

Highload UL 0.006 sb0.125 sc0.879 s

Maximum DL Packet Sizea640 bytes 2042 bytes 14400 bytes

Baseload DL 1.20 s 2.40 s 2.40 s

Highload DL 0.039 sb0.125 sc0.879 s

a) UL OH is assumed to be 31 % and DL OH is assumed to be 29 %

b) Indicates packet latency requirements that will very likely be 

exceeded by node processing and other higher layer latency 

contributions, i.e., ‘Latency OH’ 

c) Indicates packet latency requirements that may be exceeded by node 

processing and other higher layer latency contributions 

Figure 71 shows the requirements for cell edge goodput to meet a specific packet time for 

different packet sizes. Obviously a larger packet size requires a higher cell edge goodput 

which will affect the threshold sensitivity and subsequently, the system gain and 

propagation range. Channel BW, channel OH, and MCS also come into play, thus for the 
165 

same cell edge goodput, there will be differences in threshold sensitivity from technology 

to technology. 

Figure 71 - Cell edge goodput vs. maximum packet size 

Even with a modest channel BW and low peak spectral efficiency, most wireless 

technologies of interest will be able to meet capacity requirements for rural and low 

density rural AMI / FAN deployments due to the very low density of end-points or actors 

and the resulting low data rate requirements. 

In urban and dense urban areas the range is severely limited due to deployments requiring 

relatively low BS antenna heights in the presence of multi-story buildings and high 

penetration losses associated with basement-located end-points. The limited range in 

these cases will result in fairly modest capacity requirements for each BS. 

The range in suburban areas will generally be greater than in urban areas due to reduced 

building clutter and more favorable outdoor end-point locations. Additionally, as shown 

in Table 20, even though the average end-point density is lower than in urban areas, the 

SG data requirement per end-point is higher. 

Predicting the network’s ability to meet latency requirements is not as straightforward as 

it is for range and capacity predictions. As one would expect, as the traffic transmitted on 

any given channel approaches the channel capacity there will be a diminishing number of 

time slots for packets queued for transmission. This channel congestion leads to longer 

packet delays and typically creates a situation in which latency becomes the limiting 

performance factor long before data traffic levels reach the channel capacity limit. In 

practice, QoS would come into play to help alleviate issues with high priority, latencysensitive payloads. One of the limitations of the modeling tool, however, is that it does 

not take QoS into account. With respect to meeting SG latency requirements, this 

limitation should be kept in mind when evaluating the results that follow in later sections. 
166 

6.7.2 Summary of Terrestrial-based Technology Submissions

There are a great number of demographic and wireless technology-related parameters that 

can be adjusted or selected when using the modeling tool. Although many of the wireless 

technology parameters are specified by an applicable standard, some parameters will be 

vendor-specific. At this point it is informative to look at a summary of the terrestrialbased wireless submissions provided by the SDOs in response to the requests made in 

section 4 with a focus on the performance parameters that directly influence the output of 

the framework and modeling tool. 

Table 24 summarizes the submissions for coverage in licensed bands in the frequency 

range of primary interest, 700 MHz to 6000 MHz. A number of submissions also offered 

solutions in licensed bands below 700 MHz, including the 450 MHz to 470 MHz IG 

Band. There were nine submissions covering frequencies from 700 MHz to 1000 MHz 

and 1400 MHz to 1900 MHz, respectively, but no wireless submissions to cover licensed 

frequency bands from: 1000 MHz to 1400 MHz, 2700 MHz to 3300 MHz, or 3700 MHz 

to 6000 MHz. 

Table 24: Summary of submitted wireless technologies coverage in license bands 

< 700 MHz 700 MHz to 

1000 MHz 

1400 MHz to 

1900 MHz 

2000 MHz to 

2700 MHz 

3300 MHz to 

3700 MHz 

4 9 9 4 4 

LTE LTE LTE LTE

WiMAX WiMAX WiMAX / 

WiGRID 

WiMAX / 

WiGRID 

HSPA+ HSPA+ HSPA+ HSPA+

WCDMA WCDMA WCDMA WCDMA

GSM-EDGE GSM-EDGE

xHRPD xHRPD xHRPD

HRPD EV-DO HRPD EV-DO HRPD EV-DO

CDMA2000 CDMA2000 CDMA2000

IEEE 802.15.4g-e IEEE 802.15.4g-e IEEE 802.15.4g-e

In addition to coverage in the licensed bands, there were submissions for the three 

unlicensed ISM bands in the US. These are summarized in Table 25. 

Table 25: Submissions for the ISM bands (unlicensed spectrum) 

902 MHz - 928 MHz 2400 MHz - 2483.5 MHz 5725 MHz – 5875 MHz 

IEEE 802.15.4g-e

IEEE 802.11ah 

IEEE 802.15.4g-e

IEEE 802.11n 

WiGRID

IEEE 802.11ac 

With respect to channel bandwidth for the licensed bands, three of the submissions 

provided a wide range of choices, whereas, five of the technologies offer only one choice 

(as shown in Table 26). 
167 

Table 26: Channel bandwidth options 

0.208 MHz 1.25 MHz 5.0 MHz 

(3.84 MHz) 

≤ 3 MHz to ≥

10 MHz 

1 2 2 3 

LTE

xHRPD HSPA+ WiMAX / 

WiGRID 

GSM EDGE CDMA2000 WCDMA HRPD EV-DO

As listed in Table 27 for duplex choices, four of the technologies offer only FDD and 

four have solutions for both FDD and TDD. Adaptive TDD when available can provide 

improved spectral efficiency for traffic that is highly asymmetric. 

Table 27: Duplex options 

FDD Only TDD or FDD Adaptive TDD 

4 4 1

xHRPD HSPA+

HRPD EV-DO WCDMA

CDMA2000 LTE

GSM EDGE WiMAX WiMAX / WiGRID

In addition to the channel BW, key parameters for estimating channel and BS capacity is 

the peak UL and peak DL modulation index. Although this information was not 

requested specifically in section 4, it can be derived from the OTA submissions for peak 

DL and UL data rates or from the references cited for the technologies in the wireless 

capabilities matrix. Table 28 lists some wireless technologies and their UL and DL 

modulations. 

Table 28: Peak UL and DL modulation 

DL 

64QAM 

LTE

WiMAX 

HSPA+ 

HRPD EV-DO 

WiGRID 

WiMAX 

(UL Optional) 

32QAM GSM EDGE

16QAM WCDMA 

CDMA2000 xHRPD 

QPSK 802.15.4g-e 

QPSK 16QAM 32QAM 64QAM UL 

6.7.3 Baseline Parameter Choices for Modeling Tool Assessment 

To maximize its utility, the SG framework and wireless modeling tool has a number of 

parameters that can be inputted or selected by the user. To enable a fair analysis for the 

purposes of this report a number of parameter choices have been made to ensure relative 

consistency with technology to technology comparisons. For the five different 
168 

demographic regions the deployment related parameter choices are shown in Table 29. 

The notes in the bottom row of the table provide the reasoning for some of the choices 

that were made. 

Table 29: Parameter choices for demographic regions

Dense Urban Urban Suburban Rural 

Low 

Density 

Rural 

Coverage Area 5 mi220 mi2100 mi21,000 mi23,000 mi2

BS Antenna 

Height1 10 m 10 m 10 m 25 m 30 m 

End-Point 

Antenna Height n/a 2 m 2 m 2 m 2 m 

End-Point 

Location2 Indoor 

Basement 

Indoor 

Business Outdoor Outdoor Outdoor 

Number of 

Alternate 

(Parallel) 

Propagation Paths3 

2 3 3 1 1 

Terrain Type4n/a n/a Type A 

BS MIMO5

(1×1) (1×1) (1×1) 

≤ 1800 MHz (1×1)

> 1800 MHz (2×2) 

> 3500 MHz (4×4) 

Number of Serial 

Links6 1 1 1 2 3 

Frequency Range71900 MHz to 

6000 MHz 700 MHz to 6000 MHz 700 MHz to 6000 MHz 

Notes 1 Higher average heights are anticipated in rural and low density rural, 

taking advantage of existing transmission towers. Heights in other regions 

consistent with typical utility pole heights. 

2 Dense Urban meter banks are typically basement-located consistent with 

underground utilities. Meter banks in urban areas are more likely to be at 

grade in alleys outdoors or in an indoor enclosure.

3 Less likely to have access to multiple BS in rural areas due to wider BS to

BS spacing and less requirement for ubiquitous coverage. Limited access in 

Dense Urban regions due to building blockage with low BS antenna heights 

4 Terrain type is only applicable for Erceg-SUI path loss model used for 

suburban and rural environments. Type A defines terrain that is ‘hilly with 

moderate to heavy tree density’. 

5 Multiple antenna options in the higher frequency bands will be more 

practical in rural areas where there is less objectionable visual impact and 

existing towers that can handle the increased wind loading 

6 Rural regions will more likely require multiple hops or links to complete an 

end-to-end connection 

7 Frequency limitations due to lack of valid path loss model for selected 

antenna heights 

Technology and network related parameter choices for the analysis are as follows: 
169 

• Latency Overhead (LOH): Inherent with any deployment will be a baseline latency 

due to node processing times at Layer 1 / Layer 2 and further processing delays in 

the higher levels. These are assumed to be: 25 millisecond per node (2 nodes per 

link) plus 50 millisecond per link. The assumed LOH is then: for 1 hop, 100 ms; 

for 2 hops, 125 ms; and for 3 hops, 150 ms. 

• Channel OH: This is an especially important performance parameter for 

technology comparative purposes but, unfortunately, a difficult metric to quantify 

since it is dependent on many different factors, including the average packet size, 

traffic type, number of end-points, etc. Most wireless technologies use a 

simulation approach to arrive at an estimate for average layer 2 data throughput 

(see Group 5 in section 4) but since the simulation parameters and assumptions 

differ between technologies and, additionally, the deployment assumptions used 

for the simulations will not typically reflect what is called for in an SG Network, 

the channel throughput and OH arrived at with this approach can only be 

considered a guideline. For the purposes of this section the following 

assumptions are made for channel OH for each of the wireless technologies that 

are analyzed: 

Nominal layer 2 DL Channel OH = 29 % and layer 2 UL Channel OH = 31 % 

(total OH including higher layers is 49 % and 51 % for DL and UL, respectively 

and no adjustment is made for different packet or frame sizes). 

For range-limited deployments any errors in the channel OH estimate will have 

little or no effect on the resulting number of required BSs or DAPs. Channel OH 

will play a role only in deployments that are latency- or capacity-limited. In those 

cases, solutions will be shown with a plus and minus channel OH variation so as 

to illustrate the sensitivity to that parameter. 

• Cell edge goodput: This is calculated to ensure a maximum time of 1 s for the 

average highload payload size for each demographic region in the DL and for an 

average packet size of 8116 bytes in the UL or a modulation-coding index of 

QPSK-1/4, whichever results in a higher cell edge goodput. 

• Packet Size: The packet size is assumed to be the maximum submitted by each of 

the technologies. For the four technologies that submitted different packet sizes 

for the DL and UL, the DL packet size is assumed for the highload assessment 

and the UL packet size for the baseload assessment. Since the modeling tool does 

not account for the packet size impact on channel OH, this impacts only the 

latency and the ability to meet the latency requirement. 

• Link availability: This is assumed to be 96 % at the cell edge, which, in turn, 

determines the value for fade margin. 
170 

• BS configuration and frequency reuse: 3-sector BSs are assumed with one 

channel per sector and a reuse factor of 1 with dedicated as opposed to shared 

spectrum (no additional margin for inter-operator interference) 

• Smart Meter Antenna Gain and Tx Power: 0 dBi and 0.5 W (+27 dBm), 

respectively 

• BS Noise Figure: <3000 MHz = 4 dB, ≥3000 MHz = 5 dB

BS Antenna Gain: There will generally be size constraints, especially with low BS 

heights. For the same size antenna the higher frequency bands have an advantage. The 

BS / DAP antenna gains assumed for the modeling tool assessment are shown in Figure 

72. 

Figure 72 - Base station (DAP) antenna gain 

• BS Tx Power and EIRP: For the unlicensed ISM bands the BS EIRP is set to 

+36 dBm (4 watts) to comply with FCC regulations. In the licensed bands the BS 

power is assumed to be 10 watts, resulting in an EIRP ranging from +50 dBm at 

700 MHz to +57 dBm at 3700 MHz, well below the regulatory maximum for 

these bands. 

6.7.4 Wireless Technology Assessment 

As discussed in section 6.5, the modeling tool makes use of five different path loss 

models to address different frequencies, deployment venues, and antenna heights. In 

some cases, there may be more than one valid model applicable for the parameter choices 

summarized above and for other cases there may not be a valid model for the selected 

parameters or deployment venue. In reviewing the results of the assessment, it is 

important to understand the basis for the path loss models assumed for the different 

scenarios that are being analyzed. 
171 

Dense Urban: The ITU M.2135-1 model is the only valid model for a 10 m BS antenna 

height in a deployment region surrounded by higher multi-story buildings. It is 

considered valid over the 2000 MHz to 6000 MHz frequency range. For the purposes of 

this assessment it has been extended to 1900 MHz, since that represents the upper end of 

many of the technologies submitted. Due to the lack of a valid model, no dense urban 

data is shown for 700 MHz. 

Urban: We do not have a generally accepted path loss model for BS antenna heights 

lower than surrounding roof tops (i.e., 10 m) for small city urban regions. For the 

purposes of this assessment, the Modified Erceg-SUI for terrain Type A is assumed. This 

is considered a reasonable assumption since, to a first order, the excess path loss due to 

small city urban building clutter, can be considered similar to the excess path loss 

resulting from the presence of hills and trees. 

Suburban: The Erceg-SUI model takes into account varied terrain types and low BS 

antenna heights and as was shown in section 5.2.1.3.9, the modified version of the model 

provides a good correlation with generally accepted foliage and diffraction models with 

respect to frequency dependence. Since the ITU M.2135-1 path loss model includes 

parameters for building heights, inserting building heights below 10 m would validate 

this model over the 2000 MHz to 6000 MHz frequency range. For the assessment results 

the Modified Erceg-SUI Model was felt to be the best choice. 

Rural and Low Density Rural: For the higher BS antenna heights assumed for the rural 

venues, the ITU M.2135-1 or the Modifies Erceg-SUI could be considered valid over the 

700 MHz to 6000 MHz frequency range. For the same reasons cited above, the Modified 

Erceg-SUI Model was used for the rural area assessments. 

The differences in the frequency dependency for predicted excess path loss can be seen 

by comparing the ITU model with the Modified Erceg-SUI model for Type A terrain. 

Compared to the Modified Erceg-SUI model, the ITU model predicts a higher excess 

pass loss at 700 MHz, comparable excess path loss at 2000 MHz and lower excess path 

loss at 6000 MHz. 

6.7.4.1 Meeting Highload Demand 

Tables are provided for three frequency bands: 700 MHz (Table 30), 1900 MHz (Table 

31), and 3550 MHz to 3700 MHz (Table 32). These tables make use of the following 

notations to display the results.

• Number of BS / DAPs: BS = Total number of BS followed by letter; R = Rangelimited, C = Capacity-limited, and L = Latency limited. When used in this 

context latency limited means the BS / DAP is limited in the number of endpoints that can be supported to meet either the minimum latency required or a 

latency specified in the model as ‘acceptable’. 

• End-points: EP = Total number of End-points per BS. Divide by 3 to get the 

number of end-points per channel (or sector). 

• Latency requirement, in seconds (L): If the minimum latency requirement, L, is 

met; L= -/L, if not met; L = ‘latency value that can be met in seconds’/L. In some 

cases LOH the will be greater than the required latency, L, these cases will have 

the notation, ‘LOH > L’.
172 

The number of supportable end-points is determined by the binomial distribution 

approach discussed in section 5.2.7 and, as previously described, this approach 

results in a more lower estimate for the number of supportable end-points as 

compared to the approach using the M/M/D queuing model. 

Percentage of BS capacity for SG: For range-limited deployments there will be excess BS 

capacity, SGL (Smart Grid Load) = % will indicate what percentage of the total 3-sector 

BS capacity is for the SG AMI / NAN. 

Table 30: Highload 700 MHz 

700 MHz 

Highload 

Dense 

Urban Urban 

Suburban 

Type A 

Rural 

Type A 

Low Density 

Rural 

Type A 

Coverage Area 5 mi2 20 mi2100 mi21,000 mi23,000 mi2

GSM EDGE

● FDD 

● BW=208 kHz 

● Pkt=1560 

n/a BS = 11 L

EP = 6267 

L = -/0.12 

BS = 10 R 

BS = 391 L

EP = 284 

L = -/0.12 

BS = 59 C 

BS = 12 R 

BS = 133 R

EP = 488 

L=0.15/0.06 

LOH > L 

SGL=0.72 % 

BS = 348 R

EP = 32 

L = 0.2/0.04 

LOH > L 

SGL=0.33 % 

802.15.4g-e 

● TDD 

● BW=1.2 MHz 

● Pkt=2047 

n/a BS = 8 R

EP = 8617 

L = -/0.16 

SGL=8.9 % 

BS = 281 L

EP = 396 

L = -/0.16 

BS = 63 C 

BS = 11 R 

BS = 110 R

EP = 590 

L=0.15/0.08 

LOH > L 

SGL=0.49 % 

BS = 286 R

EP = 39 

L = 0.2/0.05 

LOH > L 

SGL=0.23 % 

WCDMA

● FDD 

● BW=3.84 MHz 

● Pkt=12750 

n/a BS = 8 R

EP = 8617 

L = -/0.97 

SGL=0.62 % 

BS = 11 R

EP = 10095 

L = -/0.97 

SGL=47.9 % 

BS = 110 R

EP = 590 

L = -/0.47 

SGL<0.1 % 

BS = 286 R

EP = 39 

L = -/0.31 

SGL<0.1 % 

HSPA+

● FDD 

● BW=3.84 MHz 

● Pkt=5274 

n/a BS = 8 R

EP = 8617 

L = -/0.40 

SGL=.71 % 

BS = 11 R

EP = 10095 

L = -/0.40 

SGL=65.2 % 

BS = 110 R

EP = 590 

L = -/0.20 

SGL<0.1 % 

BS = 286 R

EP = 39 

L = 0.2/0.13 

LOH > L 

SGL<0.1 % 

CDMA2000

● FDD 

● BW=1.25 MHz 

● Pkt=480 

n/a BS = 8 R

EP = 8617 

L=0.15/0.04 

LOH > L 

SGL=1.90 % 

BS = 24 L

EP = 4627 

L=0.15/0.04 

LOH > L 

BS = 16 C 

BS = 11 R 

BS = 110 R

EP = 590 

L=0.15/0.02 

LOH > L 

SGL=0.17 % 

BS = 286 R

EP = 39 

L=0.20/0.01 

LOH > L 

SGL<0.1 % 

HRPD EV-DO

● FDD 

● BW=1.25 MHz 

● Pkt=1024 

n/a BS = 8 R

EP = 8617 

L=0.15/0.08 

LOH > L 

SGL=1.34 % 

BS = 17 L

EP = 6532 

L=0.15/0.08 

LOH > L 

BS = 11 RC 

BS = 110 R

EP = 590 

L=0.15/0.04 

LOH > L 

SGL<0.1 % 

BS = 286 R

EP = 39 

L=0.2/0.03 

LOH > L 

SGL<0.1 % 
173 

700 MHz 

Highload 

Dense 

Urban Urban 

Suburban 

Type A 

Rural 

Type A 

Low Density 

Rural 

Type A 

Coverage Area 5 mi2 20 mi2100 mi21,000 mi23,000 mi2

xHRPD

● FDD 

● BW=1.25 MHz 

● Pkt=640 

n/a BS = 8 R

EP = 8617 

L=0.15/0.05 

LOH > L 

SGL=1.90 % 

BS = 25 L

EP = 4442 

L=0.15/0.05 

LOH > L 

BS = 16 C 

BS = 11 R 

BS = 110 R

EP = 590 

L=0.15/0.02 

LOH > L 

SGL=0.17 % 

BS = 286 R

EP = 39 

L=0.20/0.01

LOH > L 

SGL<0.1 % 

LTE

● FDD 

● BW=5 MHz 

● Pkt=8188 

n/a BS = 8 R

EP = 8617 

L = -/0.60 

SGL=0.37 % 

BS = 11 R

EP = 10095 

L = -/0.6 

SGL=30.0 % 

BS = 110 R

EP = 590 

L = -/0.3 

SGL<0.1 % 

BS = 286 R

EP = 39 

L = -/0.2 

SGL<0.1 % 

WiMAX

● FDD 

● BW=5 MHz 

● Pkt=2042 

n/a BS = 8 R

EP = 8617 

L = -/0.15 

SGL=0.37 % 

BS = 11 R

EP = 10095 

L = -/0.15 

SGL=30.0 % 

BS = 110 R

EP = 590 

L=0.15/0.08 

LOH > L 

SGL<0.1 % 

BS = 286 R

EP = 39 

L = 0.2/0.05 

LOH > L 

SGL<0.1 % 

700 MHz Summary

● BS for Range 

● BS for Capacity 

● BS for Latency 

n/a 8 to 10 

8 to 11 

11 to 12 

11 to 63 

11 to 391 

110 to 133 286 to 348 

Table 31: Highload 1900 MHz 

1900 MHz 

Highload 

Dense 

Urban Urban 

Suburban 

Type A 

Rural 

Type A 

Low 

Density 

Rural 

Type A 

Coverage Area 5 mi2 20 mi2100 mi21,000 mi23,000 mi2

GSM EDGE

● FDD 

● BW=208 kHz 

● Pkt =1560 

BS = 64 R

EP = 1111 

L =0.15/.12

TPKT > L 

SGL=1.94 % 

BS = 41 R

EP = 1682 

L = -/0.12 

SGL=1.88 % 

BS= 1158 L

EP = 96 

L = -/0.12 

BS = 69 C 

BS = 28 R 

BS = 232 R

EP = 280 

L = 0.15/.06 

LOH > L 

SGL=0.17% 

BS = 597 R

EP = 19 

L = 0.2/.04 

LOH > L 

SGL<0.1 % 

WCDMA

● FDD 

● BW=3.84 MHz 

● Pkt=12750 

BS = 49 R

EP = 1451 

L = -/0.97 

SGL=0.24 % 

BS = 35 R

EP = 1970 

L = -/0.97 

SGL=0.15 % 

BS = 26 R

EP = 4271 

L = -/0.97 

SGL=20.8 % 

BS = 192 R

EP = 338 

L = -/0.47 

SGL<0.1 % 

BS = 491 R

EP = 23 

L = -/0.31 

SGL<0.1 % 

HSPA+

● FDD 

● BW=3.84 MHz 

● Pkt=5274 

BS = 49 R

EP = 1451 

L = -/0.40 

SGL=0.13 % 

BS = 35 R

EP = 1970 

L = -/0.40 

SGL<0.1 % 

BS = 26 R

EP = 4271 

L = -/0.40 

SGL=14.9 % 

BS = 192 R

EP = 338 

L = -/0.20 

SGL<0.1 % 

BS =491 R

EP = 23 

L=0.2/0.13 

LOH > L 

SGL<0.1 % 
174 

1900 MHz 

Highload 

Dense 

Urban Urban 

Suburban 

Type A 

Rural 

Type A 

Low 

Density 

Rural 

Type A 

Coverage Area 5 mi2 20 mi2100 mi21,000 mi23,000 mi2

CDMA2000

● FDD 

● BW=1.25 MHz 

● Pkt=480 

BS = 49 R

EP = 1451 

L=0.15/0.04 

LOH > L 

SGL=0.73 % 

BS = 35 R

EP = 1970 

L=0.15/0.04 

LOH > L 

SGL=0.51 % 

BS = 26 R

EP = 4271 

L=0.15/0.04 

LOH > L 

SGL=63.9 % 

BS = 192 R

EP = 338 

L=0.15/0.02 

LOH > L 

SGL<0.1 % 

BS = 491 R

EP = 23 

L=0.2/0.01 

LOH > L 

SGL<0.1 % 

HRPD EV-DO

● FDD 

● BW=1.25 MHz 

● Pkt=1024 

BS = 49 R

EP = 1451 

L = 0.15/.08 

LOH > L 

SGL=0.65 % 

BS = 35 R

EP = 1970 

L = 0.15/.08 

LOH > L 

SGL=0.32 % 

BS = 26 R

EP = 2023 

L =0.15/.08 

LOH > L 

SGL=45.8 % 

BS = 192 R

EP = 338 

L =0.15/.04 

LOH > L 

SGL<0.1 % 

BS = 491 R

EP = 23 

L = 0.2/.03 

LOH > L 

SGL<0.1 % 

xHRPD

● FDD 

● BW=1.25 MHz 

● Pkt=640 

BS = 49 R

EP = 1451 

L=0.15/0.05 

LOH > L 

SGL=0.73 % 

BS = 35 R

EP = 1970 

L=0.15/0.05 

LOH > L 

SGL=0.45 % 

BS = 26 R

EP = 4271 

L=0.15/0.05 

LOH > L 

SGL=63.9 % 

BS = 192 R

EP = 338 

L=0.15/0.02

LOH > L 

SGL<0.1 % 

BS = 491 R

EP = 23 

L=0.2/0.01

LOH > L 

SGL<0.1 % 

LTE

● FDD 

● BW=3 MHz 

● Pkt=8188 

BS = 49 R

EP = 1451 

L = -/0.60 

SGL=0.28 % 

BS = 35 R

EP = 1970 

L = -/0.60 

SGL=0.17 % 

BS = 26 R

EP = 4271 

L = -/0.6 

SGL=21.4 % 

BS = 192 R

EP = 338 

L = -/0.3 

SGL<0.1 % 

BS = 491 R

EP = 23 

L = -/0.2 

SGL<0.1 % 

WiGRID

● A-TDD 

● BW=5 MHz 

● Pkt=14400 

BS = 49 R

EP = 1451 

L = -/1.1 

SGL=0.25 % 

BS = 35 R

EP =1970 

L = -/1.1 

SGL=0.13 % 

BS = 26 R

EP = 4271 

L = -/1.1 

SGL=18.3 % 

BS = 192 R

EP = 338 

L = -/0.55 

SGL<0.1 % 

BS = 491 R

EP = 23 

L = -/0.35 

SGL<0.1 % 

1900 MHz

Summary 

● BS for Range 

● BS for Capacity 

● BS for Latency 

49 to 64 35 to 41 26 to 28 

26 to 69 

26 to 1158 

192 to 232 491 to 597 

Table 32: Highload 3550 MHz to 3700 MHz 

3550 MHz to 

3700 MHz 

Highload 

Dense 

Urban Urban 

Suburban 

Type A 

Rural 

Type A 

Low Density 

Rural 

Type A 

Coverage Area 5 mi2 20 mi2100 mi21,000 mi23,000 mi2

WCDMA 

(3550 MHz) 

● FDD - 3550 

● BW=3.84 MHz 

● Pkt=12750 

BS = 200 R

EP = 356 

L = -/0.97 

SGL<0.1 % 

BS = 116 R

EP = 595 

L = -/0.97 

SGL<0.1 % 

BS = 50 R

EP = 2221 

L = -/0.94 

SGL=10.4 % 

BS = 267 R

EP = 243 

L = -/0.47 

SGL<0.1 % 

BS =676 R

EP = 17 

L = -/0.31 

SGL<0.1 % 
175 

3550 MHz to 

3700 MHz 

Highload 

Dense 

Urban Urban 

Suburban 

Type A 

Rural 

Type A 

Low Density 

Rural 

Type A 

Coverage Area 5 mi2 20 mi2100 mi21,000 mi23,000 mi2

HSPA+ 

(3550 MHz) 

● FDD – 3550 

● BW=3.84 MHz 

● Pkt=2874 

BS = 200 R

EP = 356 

L = -/0.40 

SGL<0.1 % 

BS = 116 R

EP = 595 

L = -/0.40 

SGL<0.1 % 

BS = 50 R

EP = 2221 

L = -/0.40 

SGL=12.1 % 

BS = 267 R

EP = 243 

L = -/0.22 

SGL<0.1 % 

BS =676 R

EP = 17 

L=0.20/0.13 

LOH > L 

SGL<0.1 % 

LTE (3700 MHz)

● TDD – 3700 

● BW=5 MHz 

● Pkt=8188 

BS = 217 R

EP = 328 

L = -/0.60 

SGL<0.1 % 

BS = 125 R

EP = 552 

L = -/0.60 

SGL<0.1 % 

BS = 52 R

EP = 2136 

L = -/0.60 

SGL=12.7 % 

BS = 276 R

EP = 235 

L = -/0.30 

SGL<0.1 % 

BS= 700 R

EP = 16 

L = -/0.20 

SGL<0.1 % 

WiGRID 

(3700 MHz) 

● A-TDD – 3700 

● BW=5 MHz 

● Pkt=14400 

BS = 217 R

EP = 328 

L = -/1.10 

SGL<0.1 % 

BS = 125 R

EP = 552 

L = -/1.10 

SGL<0.1 % 

BS = 52 R

EP = 2136 

L = -/1.10 

SGL=9.1 % 

BS = 276 R

EP = 235 

L = -/0.55 

SGL<0.1 % 

BS = 700 R

EP = 16 

L = -/0.35 

SGL<0.1 % 

3700 MHz

Summary 

● BS for Range 

● BS for Capacity 

● BS for Latency 

200 to 217 125 to 143 50 to 52 267 to 276 676 to 700 

The difference in results for the frequency band assessment in the table above is directly 

attributable to the difference in path loss between 3550 MHz and 3700 MHz. 

6.7.4.2 Meeting Baseload Demand 

Of the nine wireless technologies analyzed with the SG framework and wireless 

modeling tool in the 700 MHz band, the five with channel BWs of 1.25 MHz and below 

were capacity- or latency-limited for a suburban deployment. The same five technologies 

however, did have sufficient capacity for the other four demographic regions. GSM 

EDGE with a 0.208 MHz channel BW was also capacity-limited in the 1900 MHz band 

for suburban deployments. 

Table 33 provides a summary of those five technologies at baseload demand. For this 

case they all meet capacity requirements for demographic regions and four out of the five 

meet the latency requirements. As expected, xHRPD, with a 96 byte maximum packet 

size limitation, results in a minimum latency requirement that is less than LOH in all of the 

demographic regions. 
176 

Table 33: Summary of five technologies in 700 MHz at baseload 

700 MHz 

Baseload 

Dense 

Urban Urban Suburban 

Rural 

Type A 

Low Density 

Rural 

Type A 

Coverage Area 5 mi2 20 mi2100 mi21,000 mi23,000 mi2

GSM EDGE

● FDD 

● BW=208 kHz 

● Pkt=1560 

n/a BS = 10 R

EP = 6894 

L = -/1.25 

SGL=1.20 % 

BS = 12 R

EP = 9254 

L = -/1.25 

SGL=58.2 % 

BS = 133 R

EP = 488 

L=-/0.63 

SGL=0.11 % 

BS = 348 R

EP = 39 

L = -/0.4 

SGL<0.1 % 

802.15.4g-e 

● TDD 

● BW=1.2 MHz 

● Pkt=2047 

n/a BS = 8 R

EP = 8617 

L = -/1.67 

SGL=0.88 % 

BS = 11 R

EP = 10095 

L = -/1.67 

SGL=37.6 % 

BS = 110 R

EP = 590 

L= -/0.83 

SGL<0.1 % 

BS = 286 R

EP = 39 

L = -/0.56 

SGL<0.1 % 

CDMA2000

● FDD 

● BW=1.25 MHz 

● Pkt=1536 

n/a BS = 8 R

EP = 8617 

L = -/1.25 

SGL=0.29 % 

BS = 11 R

EP = 10095 

L = -/1.25 

SGL=12.3 % 

BS = 110 R

EP = 590 

L= -/0.63 

SGL<0.1 % 

BS = 286 R

EP = 39 

L = -/0.40 

SGL<0.1 % 

HRPD EV-DO

● FDD 

● BW=1.25 MHz 

● Pkt=4608 

n/a BS = 8 R

EP = 8617 

L = -/2.50 

SGL=0.23 % 

BS = 11 R

EP = 10095 

L = -/2.50 

SGL=9.8 % 

BS = 110 R

EP = 590 

L = -/1.25 

SGL<0.1 % 

BS = 286 R

EP = 39 

L = -/0.83 

SGL<0.1 % 

xHRPD

● FDD 

● BW=1.25 MHz 

● Pkt=96 

n/a BS = 8 R

EP = 8617 

L = 0.12/0.1 

LOH > L 

SGL=0.29 % 

BS = 11 R

EP = 10095 

L=0.12/0.10 

LOH > L 

SGL=12.3 % 

BS = 110 R

EP = 590 

L=0.15/0.05 

LOH > L 

SGL<0.1 % 

BS = 286 R

EP = 39 

L =0.2/0.03

LOH > L 

SGL<0.1 % 

6.7.4.3 Wireless Assessments for the ISM bands in a PMP Topology 

The IEEE 802.11, IEEE 802.15.4g-e, and WiGRID solutions for the ISM bands all 

support a mesh topology, a topology that is beyond the capability of the SG framework 

and wireless modeling tool. Nevertheless, it can be informative to assess these 

technologies assuming a PMP topology as long as the results are not used to draw direct 

comparisons to the wireless solutions in the licensed bands summarized above. Although 

the EIRP regulatory limit in the ISM bands significantly reduces the range and coverage 

capability for a PMP topology, the support for mesh will increase the effective coverage 

area well beyond what is predicted for PMP. What the PMP analysis does provide for 

ISM band solutions is, at least, a qualitative assessment for relative variations in coverage 

due to frequency and demographic differences due to end-point locations and varied BS 

antenna heights. Additionally, the analysis shows that unless mesh can be supported, the 

ISM solutions would not be a practical solution in a dense urban deployment in the 

higher frequency bands unless an intermediate AP were used to aggregate the basementlocated end-points as described in section 6.4. 

Table 34 shows the summary of these three technologies at highload demand assuming 

PMP. 
177 

Table 34: Highload ISM bands 

ISM Bands 

BS EIRP = 

36 dBm 

Highload 

Dense 

Urban Urban 

Suburban 

Type A 

Rural 

Type A 

Low Density 

Rural 

Type A 

Coverage Area 5 mi2 20 mi2100 mi21000 mi23000 mi2

802.15.4g-e 

915 MHz 

• TDD 

• BW=1.2 MHz 

• Pkt=2047 

No valid path 

loss model 

BS = 14 R

EP = 4924 

L = -/0.16 

SGL=6.03 % 

BS = 281 L

EP = 396 

L = -/0.16 

BS = 64 C 

BS = 16 R 

BS = 168 R

EP = 386 

L=0.15/0.08 

LOH > L 

SGL=0.5 % 

BS = 438 R

EP = 25 

L=0.2/0.05 

LOH > L 

SGL=0.15 % 

802.11ah 

915 MHz 

• TDD 

• BW=5 MHz 

• Pkt=1500 

No valid path 

loss model 

BS = 14 R

EP = 4924 

L = -/0.12 

SGL=3.62 % 

BS = 309 L

EP = 360 

L = -/0.12 

BS = 39 C 

BS = 16 R 

BS = 168 R

EP = 386 

L=0.15/0.06 

LOH > L 

SGL<0.1 % 

BS = 438 R

EP = 25 

L=0.2/0.04 

LOH > L 

SGL<0.1 % 

802.15.4g-e 

2450 MHz 

• TDD 

• BW=1.2 MHz 

• Pkt=2047 

BS = 641 R

EP = 111 

L = -/0/16 

SGL=0.25 % 

BS = 97 R

EP = 111 

L = -/0/16 

SGL=0.87 % 

BS = 464 L

EP = 240 

L = -/0.16 

BS = 74 C 

BS = 59 R 

BS =470 R

EP = 138 

L=0.15/0.08 

LOH > L 

SGL<0.1 % 

BS=1223 R

EP = 9 

L=0.2/0.05 

LOH > L 

SGL<0.1 % 

802.11n 

2450 MHz 

• TDD 

• BW=5 MHz 

• Pkt=1500 

BS = 641 R

EP = 111 

L = -/0/12 

SGL<0.1 % 

BS = 97 R

EP = 111 

L = -/0/12 

SGL<0.1 % 

BS = 59 R

EP = 883 

L = -/0.12 

SGL=4.31 % 

BS =470 R

EP = 138 

L=0.15/0.06 

LOH > L 

SGL<0.1 %

BS=1223 R

EP = 9 

L=0.2/0.04 

LOH > L 

SGL<0.1 % 

801.11ac 

5800 MHz 

• TDD 

• BW=5 MHz 

• Pkt=1500 

Range

< 20 m 

BS = 980 R

EP = 71 

L = -/0.12 

SGL<0.1 % 

BS = 263 R

EP = 423 

L = -/0.12 

SGL = 1.0 % 

BS =1646 R

EP = 40 

L=0.15/0.06 

LOH > L

SGL<0.1 % 

BS=4306 R

EP = 3 

L= 0.2/0.04 

LOH > L

SGL<0.1 % 

WiGRID 

5800 MHz 

• A-TDD 

• BW=5 MHz 

• Pkt=14400 

Range

< 20 m 

BS = 980 R

EP = 71 

L = -/1.10 

SGL<0.1 % 

BS = 263 R

EP = 423 

L = -/1.10 

SGL=1.52 % 

BS =1646 R

EP = 40 

L = -/0.55 

SGL<0.1 % 

BS=4306 R

EP = 40 

L = -/0.35 

SGL<0.1 % 

Support for mesh can offer considerable coverage and availability benefits but one must 

also take care to assess the potential latency issues that may arise when a large number of 

hops are required to maintain an end-to-end communications path. 

6.7.4.4 Meeting Latency Requirements 

What stands out in the PMP technology assessments, for both highload and baseload 

demand, is the number of scenarios for which the latency requirement is less than the 

node processing time (LOH > L) used in the binomial distribution latency model. It is 

informative to look at a few of the key SG-Network Task Force Smart Grid application 

payloads that are driving these low latency requirements and how the per link latency 
178 

requirement is impacted by actor-to-actor latency requirements and the maximum packet 

size supported by the technology that is being assessed. 

The maximum packet sizes supported by the terrestrial-based wireless technologies 

submitted, range from 96 bytes to 14,400 bytes and are summarized in Table 35. 

Table 35: Number of technologies supporting packet size ranges 

14400 bytes to 

12750 bytes 

8188 bytes to 

4608 bytes 

2874 bytes to 

1024 bytes 

640 bytes to 

96 bytes 

2 3 7 2

WiGRID 

WCDMA 

LTE 

HSPA+ (DL) 

HRPD EV-DO (UL) 

HSPA+ (UL) 

WiMAX 

CDMA2000 (UL) 

GSM-EDGE 

IEEE 802.11 

IEEE 802.15.4g-e 

HRPD EV-DO (DL) 

xHRPD (UL & DL) 

CDMA2000 (DL) 

In the AMI, FAN, or NAN network there will be actor-to-actor data-flows that are peerto-peer data flows or use a DAP as an intermediate actor bridge between the actors. For 

the peer-to-peer actor data flows, they were specifically changed to an actor to DAP and 

DAP to the other actor data flow to accommodate the Wireless Model, thus requiring that 

the total business application peer-to-peer actor-to-actor latency requirement be divided 

by two to get the per actor to DAP or DAP to actor link latency. These data paths 

generally involve the field tool. 

Another general observation is that the majority of the latency-sensitive application 

payloads are associated with firmware and program use cases for which larger sized 

payloads are necessary. These use cases are the key drivers for the highload demand and 

even though the payload latency requirement for these cases is quite modest, the larger 

payloads must be divided into packet sizes to comply with the maximum packet size 

supported by the wireless technology of interest. The per-link latency is then divided by 

the total number of packets required to carry the entire payload plus the overhead bits. 

For a business payload latency requirement of several minutes the resulting packet 

latency requirement will be significantly less than 1 s and in some cases, in the 

millisecond range, with smaller sized packets. 

Table 36 provides a summary of the per packet latency requirement per link in seconds 

for some representative data-flows taken from the SG-Network Task Force Requirements 

for business application payloads that result in a per packet latency requirement less than 

1 second. The field tool case assume an Actor-to-DAP-to-Actor data –flow, whereas the 

DAP and FAN gateway (GW) cases assume an Actor-to-DAP or DAP-to-Actor data 

flow. The packet overhead for all scenarios is assumed to be 50 %. 
179 

Table 36: Summary of per packet latency requirement per link 

Payload 

Description 

LatencyPayload Size in bytesBaseload or HighloadTo-From Actor

Maximum Packet Size 

>12750 bytes 

8188 bytes to 

4608 bytes 

2874bytes to 

1024 bytes 

640 bytes to 480 

bytes 

96 bytes 

Latency per Packet per Link in 

seconds 

Fdr-dev_cntl_ 

firmware_update_ 

cmd 

<10 

min 

400k2000k High Field 

Tool >0.96 0.61 - 0.35 

0.22-

0.08 

0.05-

0.04 0.007

DAP_firmware_

update_cmd 

<5 

min 

400k750k High Field 

Tool 

0.82-

0.45 

0.29-

0.10 

0.06-

0.05 0.010

Metrology_

program_update_ 

cmd 

<1 

min 

25k50k High Field 

Tool 

0.86-

0.31 

0.19-

0.14 0.029

FeederFault_Detect

or_sensor_data_ 

resp-data 

< 3 s 1000 Both DAP 0.75-

0.60 0.136

FeederCapBank_ne

w_confg_cmd < 3 s 500 Both DAP 0.25 

FeederCapBank_op

en_cmd < 3 s 150 Both DAP 0.75 

Dstr_cust_storage_ 

status_resp-data < 2 s 50 Both FAN GW 0.50 

As Table 36 shows the large payloads associated with firmware updates can result in 

packet latency requirements less than 100 ms for smaller sized packets. This is in the 

range of expected node processing times and will result in a situation where the latency 

requirement, L, will be less than the latency overhead, LOH. 

It is also important to note that the SG-Network Task Force Requirements for application 

payload sizes do not take into account any type of data encoding schemes that might 

reduce the size of the actual payloads that are transmitted across various networks 

segments. Another factor not accounted for is the additional overhead that would occur 

with smaller packet sizes. Since each packet would typically require a fixed number of 

overhead bits, the percent OH would increase with smaller packet sizes. Since these two 

factors will offset each other, the net impact is not clear without having additional 

information. 
180 

6.7.4.5 Sensitivity Analysis 

To facilitate the assessment of the nine terrestrial-based wireless technologies, 

assumptions were made for some key wireless parameters. In the next few paragraphs 

we will look at the relative sensitivity of some of these parameters and other factors that 

influence the number of BSs (or DAPs) necessary to meet Smart Grid requirements for an 

AMI / NAN deployment. 

Specifically we look at the relative impact of: 

• Channel OH 

• Packet Size versus Latency Requirement 

• Channel Bandwidth 

• Terrain Type 

• Link Budget and System Gain 

Channel OH: As mentioned earlier, getting an accurate estimate for total channel OH 

can be a daunting task. That said, for the purposes of the assessment, we assumed 49 % 

for the DL channel OH and 51 % for the UL channel OH. For suburban deployments in 

the 700 MHz band five of the technologies with channel BWs of 1.25 MHz or less did 

not meet the highload minimum latency requirement. With a relaxed latency requirement 

four of these technologies were still capacity-limited. The technologies with larger 

channel BWs, on the other hand, easily met capacity requirements in all five 

demographic regions. With respect to latency, however, some were impacted by the 

maximum supportable packet size in the rural and low density rural deployments where 

we assumed 2 and 3 links, respectively. Based on the model it would take a maximum 

packet size greater than approximately 6000 bytes to meet the latency requirements in 

these areas. 

The sensitivity to either a lower or higher channel OH is illustrated in Figure 73. The 

graph provides a view of the DAP and End-point count for a range of UL channel OH 

values for CDMA2000 in the 700 MHz band for a suburban Type A deployment. A 

fixed latency requirement of 200 ms (0.2 seconds) is assumed to determine the number of 

supportable end-points per channel. 
181 

Figure 73 - CDMA2000, 700 MHz, suburban highload, 0.2 second latency 

Packet Size and Latency Requirements: Both packet size and channel goodput play a 

role in determining latency performance. It is important to mention again that QoS is not 

taken into account in this technology assessment. Whereas QoS enables the assignment 

of higher priorities to more latency-sensitive payloads and packets, the model assumes all 

packets have the same priority. Despite this limitation, the model is useful in providing 

some insights as to how the different wireless parameters relate to the technology’s 

ability to meet latency requirements. 

As the assessment results indicate, with a constrained channel BW meeting the minimum 

latency requirement may require a substantial increase in the number of BSs. With a 

0.208 MHz channel BW limitation, GSM EDGE is limited to 284 end-points per BS (95 

per channel) to meet a 0.12 s minimum latency requirement. As illustrated in Figure 74, 

a modest relaxation in the latency requirement would greatly enhance the supportable 

end-points per channel and reduce the number of BSs required. With a latency of 2 or 

more seconds, the number of BS approaches 59, the number required to meet capacity 

requirements. 
182 

Figure 74 - GSM EDGE at 700 MHz, suburban Type A, highload demand 

The minimum latency requirement is inversely proportional to the maximum packet size, 

so it is also of interest to look at how the maximum packet size impacts BS requirements. 

Using GSM EDGE as an example again, Figure 75 shows the end-points per channel and 

BS requirements for packet sizes larger than the 1560 bytes listed in the wireless 

capabilities matrix for GSM EDGE. It is not clear how much flexibility there is with this 

parameter but as the chart indicates, an increased packet size results in a dramatic 

decrease in the number of required BSs. 

Figure 75 - Increasing the maximum packet size for GSM EDGE 

Another example that is interesting to look at in more detail is xHRPD. This technology 

has the lowest maximum packet size, 96 bytes for the UL, of the nine technologies 

reviewed. At 96 bytes, the minimum latency requirement is less than the latency 

overhead for all demographic regions at all frequencies supported by xHRPD. For a 

suburban deployment and highload demand, the 96 byte packet size reduced the 

minimum latency requirement to 7 ms. The latency that can be achieved with 19 BS is 
183 

0.15 s. As described earlier, one of the benefits of a smaller packet size is an increased 

probability the packet will fall within a specified latency period, in this case, 0.15 s. As 

shown in Figure 76, with an increase in packet size the BS count goes up due to the drop 

in probability until the packet size gets to about 2000 bytes. Although more BSs are 

required, the minimum latency requirement can be met at that point. Beyond 2000 bytes 

the minimum latency requirement increases more quickly than the packet size resulting in 

an increasing probability and a decreasing BS count. At about 13000 bytes, the BS count 

is at the same level it was at 96 bytes, but instead of missing the minimum latency 

requirement by more than 20 times, the latency requirement can be met with the larger 

packet size. 

Figure 76 - Relationship between packet size and BS requirements 

Channel BW: Figure 77 illustrates how channel BW affects the BS requirements 

necessary to meet capacity requirements for a suburban deployment. At approximately 

135 kHz for baseload and 2.5 MHz for highload, the deployment transitions from 

capacity-limited to range-limited. 
184 

Figure 77 - Channel BW for meeting capacity requirements 

To account for latency, as well as capacity, it would be necessary to plan for excess 

channel capacity by an amount inversely related to the maximum packet size. Typical 

numbers for highload are as presented in Table 37. 

Table 37: Packet size, channel BW, meets 

Approx. Packet Size Channel BW Meets 

- ≥ 2.5 MHz Capacity only

< 1500 bytes 5.0 MHz Capacity, Latency = 0.15 s (LOH > L)

1500 bytes to 2000 bytes ~ 4.5 MHz (+80 %) Capacity and Latency

2000 bytes to 3000 bytes ~ 4.0 MHz (+60 %) Capacity and Latency

≥ 3000 bytes ~ 3.5 MHz (+40 %) Capacity and Latency

Terrain Type: From a propagation perspective the wireless assessments summarized in 

the preceding tables (Table 30 through Table 34) assume a worst case scenario for 

suburban, rural, and low density rural regions by assuming terrain ‘Type A’. This terrain 

type is defined as ‘hilly with moderate to heavy tree density’. Although there will be 

more extreme cases than this, Type A represents the most extreme case for which we 

have a valid path loss model. Terrain types that are more propagation friendly are Types 

B and C. Figure 78 shows the reduced BS requirements for deployments in terrain Types 

B and C relative to Type A. The propagation benefits of more favorable terrain are less 

significant in suburban areas where a lower BS antenna height (10 m vs. 30 m) is 

assumed. 
185 

Figure 78 - Relative number of BSs vs. terrain type

Link Budget and System Gain: System gain, which is the major component of the link 

budget, is not generally defined by wireless standards organizations. Although the 

wireless standard may set some guidelines, there is usually considerable latitude left to 

equipment vendors for the parameters that comprise system gain. That said, one can 

expect some variations from the system gain parameters assumed for this assessment. 

The other terms used to determine the link budget include fade margin, penetration loss, 

and interference margin. The same values have been applied to all of the technologies for 

this assessment. Of these, interference margin may differ somewhat between 

technologies, but probably not more than 1 dB or 2 dB. 

Figure 79 shows the difference in BS requirements for coverage for a link budget range 

from -3 dB to +3 dB. As the chart indicates, a couple of dB can make a significant 

difference. 
186 

Figure 79 - Link budget impact on BS requirements 

6.7.5 Assessment results summary 

The SG framework and wireless modeling tool has been used to assess nine terrestrially 

based wireless technologies using parameters submitted for the Wireless Capabilities 

Matrix described in section 4 with other assumed deployment and wireless variables 

described earlier in this section. The Smart Grid requirements for this analysis is based 

on a FAN or NAN network with end-points estimates based on US census data and 

generalized parsing of the house-holds to specific categories / types of smart grid endpoints. 

Some general observations are: 

• Suburban: This demographic SG deployment area represents the greatest 

challenge from a capacity and latency perspective, especially in the lower 

frequency bands. For solutions with Channel BW constraints either additional 

channels or more BSs are necessary. Even when range-limited, SG requirements 

consumed a significant portion of the BS capacity. This could limit the use of 

existing public or other shared networks. 

• Dense Urban and Urban: With the range limited by less favorable end-point

locations, capacity requirements for SG typically consumed less than 1 % of the 

available BS capacity with the exception of GSM EDGE for urban deployment in 

the 700 MHz band due its limited channel BW. 

• Rural and Low Density Rural: Capacity requirements were typically at about 

0.1 % or below of the available BS capacity for all technologies. Latency was an 

issue with many of the technologies where we assumed 2 and 3 serial links, 

respectively for Rural and Low Density Rural. This was alleviated with larger 

packet sizes and of course QoS can also play a role. On the other hand, there will 

be many cases where a greater number of links will be required and in some cases 

a satellite link might have to be included for these regions. Meeting latency 

-40 %

-30 %

-20 %

-10 %

0 %

10 %

20 %

30 %

40 %

50 %

-4 dB -3 dB -2 dB -1 dB 0 dB 1 dB 2 dB 3 dB 4 dB

Link Budget vs. BSs for Coverage

Rural Type A

Rural Type C

Dense Urban
187 

requirements for large latency-sensitive payloads are likely to be an on-going 

challenge in these areas. 

Table 38 provides a perspective on deployment requirements with respect to: 

Technology, Frequency, and Demographic Region. The entries in the table show the 

requirements for each technology for an average US state as a proxy for a specific SG 

deployment area that includes some combination of the end-point density categories, 

arrived at by estimating the number of BSs or DAPs necessary to cover 2 % of the total 

US land area for each demographic region. It is informative to summarize requirements 

solely on the basis of coverage, even though some of the suburban area deployments are 

latency- or capacity-limited, since in many of those cases the limitations can be addressed 

by adding more channels rather than adding more BSs. Additionally, QoS, supported by 

most36 of the assessed technologies, can address many of the latency limitations. 

There is no data for Dense Urban in the 700 MHz band, since we have not found a 

suitable path loss model for BS antenna heights below surrounding roof tops. 

Table 38: Summary of assessment results assuming range-limited deployments 

Demographic 

Region 

Dense 

Urban Urban Sub-urban Rural 

Low 

Density 

Rural 

Totals 

1/50 US Land Area 36.1 449 2,306 16,127 51,310 70,228 

Total End-Points 512,627 1,547,290 2,562,144 1,048,285 205,240 5,875,586 

Type A Type A Type A 

700 MHz Totals for 700 MHz exclude dense urban

802.15.4g-e

1

, 

CDMA20001, 

HRPD EV-DO1, 

xHRPD1

, WCDMA, 

HSPA+, LTE, 

WiMAX 

n/a 180 254 1,775 4,892 7,101 

GSM EDGE1 n/a 225 277 2,145 5,952 8,599 

1900 MHz

CDMA20002, 

HRPD EV-DO2, 

xHRPD, WCDMA, 

HSPA+, LTE, 

WiGRID 

354 786 600 3,097 8,398 13,235 

GSM EDGE2462 921 646 3,742 10,211 15,982 

3550 MHz

WCDMA, HSPA+ 1,443 2,604 1,154 4,307 11,562 21,070 

3700 MHz 

LTE, WiGRID 1,566 2,806 1,200 4,452 11,973 21,997 

 

36 IEEE 802.15.4g-e is a data only solution and does not support traffic priorities. 
188 

Demographic 

Region 

Dense 

Urban Urban Sub-urban Rural 

Low 

Density 

Rural 

Totals 

1/50 US Land Area 36.1 449 2,306 16,127 51,310 70,228 

Total End-Points 512,627 1,547,290 2,562,144 1,048,285 205,240 5,875,586 

Type B Type B Type B 

700 MHz Totals for 700 MHz exclude dense urban

802.15.4g-e

1

,

CDMA20001, 

HRPD EV-DO1

, 

xHRPD1, WCDMA, 

HSPA+, LTE, 

WiMAX 

n/a 180 227 930 2,561 3.898 

GSM EDGE1n/a 225 247 1,123 3,116 4.711 

1900 MHz

CDMA20002, 

HRPD EV-DO2, 

xHRPD, WCDMA, 

HSPA+, LTE, 

WiGRID 

354 786 519 1,622 4,396 7,677 

GSM EDGE2462 921 559 1,959 5,345 9,246 

3550 MHz

WCDMA, HSPA+ 1,443 2,604 981 2,255 6,052 13,335

3700 MHz

LTE, WiGRID 1,566 2,806 1,020 2,331 6,268 13,991 

Type C Type C Type C

700 MHz Totals for 700 MHz exclude dense urban

802.15.4g-e

1

,

CDMA20001, 

HRPD EV-DO1, 

xHRPD1, WCDMA, 

HSPA+, LTE, 

WiMAX 

n/a 180 209 604 1,665 2,658 

GSM EDGE1 n/a 225 228 730 2,026 3,209 

1900 MHz

CDMA20002, 

HRPD EV-DO2, 

xHRPD, WCDMA, 

HSPA+, LTE, 

WiGRID 

354 786 468 1,054 2,858 5,520 

GSM EDGE2462 921 504 1,274 3,475 6,636 

3550 MHz

WCDMA, HSPA+ 2,020 2604 875 1,466 3,934 10,322

3700 MHz

LTE, WiGRID 1,566 2806 910 1,515 4,074 10,871 
189 

Demographic 

Region 

Dense 

Urban Urban Sub-urban Rural 

Low 

Density 

Rural 

Totals 

1/50 US Land Area 36.1 449 2,306 16,127 51,310 70,228 

Total End-Points 512,627 1,547,290 2,562,144 1,048,285 205,240 5,875,586 

Composite View A=16.4 % 

B=32.0 % 

C=51.6 % 

A=25.4 

% 

B=34.6 

% 

C=40.0 

% 

A=26.4 % 

B=28.0 % 

C=45.6 % 

700 MHz Totals for 700 MHz exclude dense urban

802.15.4g-e

1

,

CDMA20001, 

HRPD EV-DO1, 

xHRPD1

, WCDMA, 

HSPA+, LTE, 

WiMAX 

n/a 180 222 1,014 2,767 4,183

GSM EDGE1 n/a 225 242 1,225 3,366 5,059

1900 MHz

CDMA20001, 

HRPD EV-DO1, 

xHRPD1

, WCDMA, 

HSPA+, LTE, 

WiMAX 

354 786 506 1,770 4,749 8,165

GSM EDGE2462 921 545 2,138 5,774 9,840

3550 MHz

WCDMA, HSPA+ 1,443 2,604 955 2,461 6,538 14,000

3700 MHz

LTE, WiGRID 1,566 2,806 993 2,543 6,771 14,679

Note 1 Additional channels or BSs will be required for 700 MHz suburban 

deployments to meet capacity and/or latency requirements 

Note 2 Additional channels or BSs will be required for 1900 MHz suburban 

deployments to meet capacity and/or latency requirements 

The assessment results are included for terrain Types A, B, and C for suburban, rural, and 

low density rural areas. It is up to the reader to decide which terrain type is more 

applicable for the geographic characteristics of the area being analyzed or more 

specifically, what percentage breakdown between terrain types is most applicable. For 

illustrative purposes, a worksheet (Tab 1a) in the SG framework and wireless modeling 

tool provides an approximate breakdown, with respect to terrain, for each of the states 

and the District of Columbia37. This information is summarized in Table 39 and Figure 

 

37 It is important to emphasize that this terrain information is only a rough approximation based on 

information collected from many different sources. It is provided for illustrative purposes and should not 

be used as a substitute for more detailed statewide GIS information, other 3D mapping techniques, or local 

observations. 
190 

80. The spread in land area percentage breakdowns for each of the terrain types 

emphasizes the need to have specific terrain information about the particular state and 

geographic area of interest. Although a majority of states appear to have a significant 

amount of the land area (Terrain Type C) that is generally favorable for terrestrial-based 

wireless coverage, it must also be noted that much of this land area is also very sparsely 

populated. 

In addition to showing the assessment results for Types A, B, and C respectively, the SG 

Model-Area average demographic breakdowns for Types A, B, and C are used, for 

illustrative purposes, to show a composite view for the SG Model-Area. 

Table 39: Approximate terrain type breakdown for combined suburban, rural, and 

low density rural in 50 US states plus the District of Columbia 

Type A Type B Type C 

State by state 

minimum 0.0 % 10.8 % 18.9 % 

State by state 

maximum 49.6 % 

3 States ≥40 % 

59.0 % 

3 States ≥45 % 

80.8 % 

8 States ≥60 % 

Average for all states 25.0 % 31.8 % 43.2 %

USA average 25.8 % 29.7 % 44.5 %

Figure 80 - Estimated US terrain type distribution 

In making use of this assessment data from a spectrum point of view, it should be noted 

that it may not be realistic to assume the same spectrum availability over a very large 

geographic area. Geographic boundaries for spectrum licenses in the US may or may not 

coincide with specific utility regions nor will they necessarily coincide with state 

boundaries. Additionally, with respect to spectrum, the different wireless solutions are 

grouped into four categories to simplify the presentation of the data. There are numerous 

frequency allocations between 1400 MHz and 2700 MHz covered by one or more of the 

0

5

10

15

20

25

≤15 % >15 % to

25 %

>25 % to

35 %

>35 % to

45 %

>45 % to

55 %

>55 % to

65 %

>65% to

75 %

>75 %

Number of States

Percent of Land Area

Estimated Terrain Distribution for 50 US States 

and District of Columbia

Type A

Type B

Type C
191 

wireless technologies. While 1900 MHz is a reasonable choice for purposes of this 

comparative assessment, it should be obvious, based on link budget and path loss 

differences alone, that a solution in the 2300 MHz band with one wireless technology 

would yield quite different results in the 1800 MHz or 1900 MHz band even though the 

wireless attributes are similar. 

The intent of this analysis has been to provide some insights as to how the SG framework 

and wireless modeling tool can be used to assess the different terrestrial-based wireless 

technologies in a PMP topology based on the mathematical path loss models described in 

section 5 and the wireless technology attributes presented in section 4. In this analysis, 

the Smart Grid AMI / NAN average data throughput and latency requirements were 

derived from the end-point densities based on SG Model-Area data. 

Despite the limitations stated earlier, Table 38 should prove useful for early planning 

purposes to assess how the different terrestrial-based wireless technologies are likely to 

perform with respect to frequency, demographics, and relative propagation conditions. 

The above assessment should also provide a perspective on the role that different wireless 

parameters play in determining the number of BSs and equipment necessary to meet SG 

AMI / NAN requirements for coverage, latency, and capacity.

6.8 Cross Wireless Technology Considerations 

In considering a Smart Grid network, it should be recognized that the network quite 

likely will not be a single homogeneous network, but will in fact likely be a network 

consisting of multiple disparate sub-networks interconnected to form an overall Smart 

Grid network system. These sub-networks could include both non shared private 

networks and shared commercial networks. Technologies will likely include both wireline and wireless networks. In addition they could utilize both standards based and 

proprietary network technologies and protocols. However, regardless of the number and 

type of sub-networks used to implement the enterprise Smart Grid network system, it is 

critical that proper attention and consideration be given to the operational and load 

characteristics of each of the sub-networks individually and collectively to ensure that the 

composite Smart Grid network system will satisfy the overall Smart Grid Systems 

requirements. 

While the overall Smart Grid System requirements will likely vary from one 

implementation to the next, they will, in general, include elements of and be driven by the 

following considerations: 

• Business Goals and Requirements 

• Regulatory Requirements 

• Security Requirements 

• System Functionality 

• System and Operational Characteristics 

o Coverage 

o Capacity and Latency 
192 

o Responsiveness 

o Availability and Reliability 

o Resiliency to failure modes and redundancy to overcome failed network 

links 

o Flexibility to accommodate system growth, changing requirements, and 

changes in technology 

The following characteristics should be carefully considered and can be used as a guide 

in formulating the requirements for the overall SG network system and each of the 

constituent sub-networks. In addition and very importantly, a Smart Grid network system 

should be implemented to support the current requirements and yet be flexible enough to 

gracefully grow and evolve to accommodate expected future requirements and 

technology enhancements. 

Important Network Characteristics 

• Intended use of the Network – What is the intended use of the network system? It 

is important to understand the intended and potential future use of the network, 

which could be exclusively for AMI, or for DA, or for HAN interconnectivity, or 

for Direct Load Control, or for a combination of these. Often an enterprise may 

focus narrowly on a particular application without fully considering future 

applications that may also be able to leverage and effectively utilize the proposed 

network infrastructure. The applications and use cases, both current and those 

projected for the future, should be considered carefully when establishing 

requirements for the SG Network and in evaluating the network capabilities in 

this overall context. Even after fully considering potential future applications, it 

is very possible that some SG sub-networks may be implemented to serve specific 

use cases where other sub-networks may serve other use cases (for example, a 

sub-network specifically implemented for DA and a separate sub-network 

implemented specifically for direct load control). However, it is also likely that 

all sub-networks will interconnect with other shared network facilities and a 

common backbone network infrastructure linking them to one or more centralized 

service facilities. Thus while each sub-network should be evaluated for its 

intended specific use, the common and shared network infrastructure should be 

evaluated for its intended composite use. 

• Size of the Network – Consideration must be given to the number of end-points, 

their function, their location, their density, and the variability of their density. 

This is particularly important for a NAN network directly linking with the endpoints. The density of the end-points in a geographic area or region along with 

their expected traffic load characteristics will directly drive the requirements for 

the capacity and latency of the network in that area. 

• Network Capacity Requirements – Consideration for the expected traffic load, 

both deterministic and non-deterministic traffic, average and bursty traffic 
193 

patterns, and the relationship of the combined traffic patterns with the use case 

latency requirements are all critical elements when considering capacity. Often, 

while it may not be known apriori what the ultimate traffic capacity requirements 

will be, especially with periodic bursty payloads, it is critical that proper analysis 

and planning of capacity requirements are conducted for all network segments 

and facilities. Insufficient planning can result in overbuilding the network, 

resulting in higher costs for no appreciable gain, or under building the network, 

potentially less costly but negatively impacting responsiveness. 

• Latency Requirements – Each application and use case will have different latency 

requirements. End-to-end latency can be significantly impacted by several factors 

including node-to-node effective data goodput (which in turn is related to: the 

application design; security risk mitigation techniques employed; the bandwidth 

capacity between the nodes; error rates; protocol efficiency; and network load), as 

well as the internal processing delays and queues introduced by intermediate 

nodes in the data path. Careful consideration must be given to the latency 

characteristics of any and all network segments utilized to establish the path 

between the source and sync nodes for all applicable use cases. The cumulative 

or combined latency of multiple network segments encountered in linking source 

and sink nodes can be significant. Equally important is consideration for any 

potential concurrent execution of multiple use cases as may occur in the SG 

system, and the impact this would have on any potential network congestion 

points. Network congestion at critical points that may occur as a result of 

concurrent use case execution may in turn introduce unexpected but significant 

additional latency that could negatively impact latency sensitive applications and 

use cases. 

• Router and Node Throughput – In addition to the throughput of the individual 

network links, consideration must be given to the processing power, packet 

throughput capacity, and internal latency of the routers, relays, or repeaters and 

other transient network nodes used to implement connectivity between the various 

network segments. 

• Spectrum and Bandwidth Requirements – For private wireless network 

components, consideration should be given for spectrum availability and the 

bandwidth that may be needed to satisfy the transport capacity requirements. 

• Geographic location and RF morphology – The terrain and other RF 

environmental and existing RF interference factors will significantly impact the 

coverage, capacity, and reliability of any wireless network. These characteristics 

should be fully considered in any areas intended to be served by wireless 

technologies. The SG framework and wireless modeling tool described in section 

6.5 and used in section 6.7 to assess various terrestrial wireless technologies 

provides a means to gain initial insights in this regard. 
194 

• Availability and Reliability – Adequate link margins for wireless networks should 

be part of the planning process to ensure satisfactory link connectivity under 

highly variable propagation conditions. 

• Resiliency and Redundancy – Requirements for network resiliency, or the ability 

of the network to tolerate failures and the requirements for network redundancy to 

route around failures should be fully considered to insure the Availability and 

Reliability requirements are met. 

• Backhaul requirements and availability – The requirements of the backhaul links 

from the NAN DAPs to the centralized systems, and the availability and 

reliability of these backhaul links. Of particular importance here is the additional 

latency or any capacity constraints that may be introduced by the backhaul 

component of the network. 

• Flexibility for Growth and other Changes – The ability to accommodate growth 

and changes in the number of Smart Grid applications, the number and type of 

end-points, and any likely or potential changes in functionality of those end-points 

are key considerations when selecting a network system and in selecting the 

technology and topology of the sub-networks. 

• Security – Security requirements are particularly important for an SG system and 

the networks supporting them. Important and significant elements of network 

security include requirements for: 

o Policies and procedures for nodes and devices joining the network 

o Protecting confidentiality of the data on the network 

o Safeguards to prevent modification or destruction of the information and 

data being transmitted over the network 

o Preventing unauthorized attachment or connections to the network 

o Authorizing and permitting data exchanges only between nodes authorized 

to do so 

Failure to recognize and properly plan for adequate network security could be 

very costly both in potential fines for regulatory violations and in the cost of any 

retrofits as may be required by governmental mandates. Additionally, corrupted 

or lost data payloads can lead to costly service disruptions, organizational 

disruptions, billing errors, etc. 

• Ease of Deployment – How capable or flexible does the network have to be to 

adapt to changes in the deployment planning, processes, and physical 

environments. 

• Ease of Monitoring and Managing the Network – The ability to provide 

comprehensive monitoring and management of the individual sub-networks, as 

well as the overall SG network system is a critical element for the ongoing 

effective operation of an SG system. 
195 

• Incremental Cost to Achieve 100 % Coverage – Typical Smart Grid applications 

require 100 % fixed node coverage; different topologies have different 

incremental costs to go from a nominal coverage to 100 % coverage. 

• Scalability – Expansion of the network as needed to adjust to the deployment 

planning, processes, and physical environments. 

The above list provides some of the key characteristics that must be carefully considered 

when establishing the requirements for the SG network system. Particular care must be 

taken when considering sub-networks (like the SG NAN systems that directly connect to 

the SG end-points), both for their intended initial use and for any expected expansion 

beyond their initial use cases which may include both new use cases and additional 

numbers of end-point devices. Proper network planning prior to deployment can lead to 

a more efficient SG network and mitigate the need for significant and costly network reengineering in the future. 

7 Conclusions 

The goals of PAP02 are to develop guidelines for the use of wireless communications in 

a smart grid environment. To date several milestones have been achieved towards these 

goals and are described in this report. 

The first significant milestone is the development of smart grid application 

communication requirements. While many use cases and scenarios have been described 

in the past, the task undertaken by OpenSG provides comprehensive and detailed sets of 

quantitative user communication requirements capturing different use cases and 

environments. These requirements are tremendously valuable to both the user and 

network technology communities in order to better understand the smart grid landscape. 

The use of these requirements is not limited to wireless technologies; but they can also be 

used for evaluating any communication technology, be it wireless or wired. 

Another milestone described in this report is a framework to evaluate wireless 

communication technologies. This is a general methodology that helps users and network 

technologists provide answers to the question: how well does wireless technology, X, 

support application requirements, Y? Rather than provide a single answer to this 

question, a framework and a set of tools are provided for users and network technologists 

to help them formulate answers that apply to their own environment. Recognizing that 

every environment is different and every user requirement may pose additional 

constraints and challenges to the network designer, this approach is more useful because 

it is universal. Proof of concept examples are also included in this report in order to 

further illustrate the concepts described and make it easier for users to develop their own 

evaluations. Additional tools and evaluation models developed by different contributors 

are referenced in this report and are available on the NIST PAP02 collaborative site: 

 http://collaborate.nist.gov/twiki-sggrid/bin/view/SmartGrid/PAP02Wireless

Also included in this report are key performance findings that are applicable to most 

environments and wireless technologies. These represent key factors to consider in the 
196 

assessment of wireless technologies such as interference, environment, coverage range, 

and deployment range extension. 

Going forward, this report may be revised as needed in order to include additional 

material contributed by PAP02 members. Additional material may include examples on 

how to combine security and communication requirements and their implications on 

performance, and additional communication requirements and wireless technology 

evaluation examples and models. 

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