Coverage, Capacity, & Resilience 

Enhancement

in Limited Public Safety Network

Hyeong-Ah Choi1*, Amrinder Arora1*, Yanxiang Zhao1; Gokhan Sahin2; Wei Cheng3

1The George Washington U.; 2Miami U.; 3U. of Washington Tacoma

1

Project Period: 6/1/2017 – 12/31/2019
2

DISCLAIMER

This presentation was produced by guest speaker(s) 

and presented at the National Institute of Standards 

and Technology’s 2019 Public Safety Broadband 

Stakeholder Meeting. The contents of this 

presentation do not necessarily reflect the views or 

policies of the National Institute of Standards and 

Technology or the U.S. Government.

Posted with permission
Agenda

Task 1: Intelligent Mobile Base 

Station Placement

Task 2: Multicast Quality-of-Service 

for PSNs

3

Project Overview

• Task 1: Intelligent, Dynamic Coverage, 

Capacity, and Resilience Enhancement

• Task 2: LTE Multicast for Public Safety 

Networks
Project Overview

4

Objectives: To support communications for First Responders 

(FRs) through

• Extending Coverage and Capacity

• Real-time Scheduling of Relay Operation (on/off) and 

Coordination

• Enhancing LTE Multicast Capabilities for PSN

• QPP-Integrated Resource Allocation in LTE Multicast

Main Tasks

Investigation of prudent use of relays and mobile eNBs to 

support FRs through trajectory and placement 

optimization, and real-time scheduling of relay operation 

and coordination

Investigation of PSN-specific LTE Multicast incorporating 

QoS, prioritization, and preemption (QPP) for

Mission-Critical Communications (MCC)
• Task 1.1: Optimal Placement of Relays and BSs in OffNetworks

• Task 1.2: Mobile Relays and Mobile BSs Trajectory 

Optimization and Scheduling

• Task 1.3: Network Resilience: FRs aided by a Mobile BS 

or Relay

• Offline/online, distributed/centralized, 

deterministic/stochastic, and hybrid algorithms and 

techniques to support Task 1.1-1.3.

Project Overview

Accomplishments – Statement of Work

5

• Task 2.1: Base Station QPP eMBMS

• Task 2.2: Base Station to Off-Network UE Multicast

• Task 2.3: UE-to-UE Multicast

• Offline/online, deterministic/stochastic, 

distributed/centralized, and hybrid algorithms and 

performance analysis for 

• eMBMS from Macro BS to support Task 2.1;

• BS to off-network multicast to support Task 2.2.

• Distributed and semi-distributed, 

deterministic/stochastic and hybrid algorithms for 

UE-to-UEs multicast to support Task 2.3.
Project Overview

Accomplishments

6

Deliverables Percentage of Completion

Task 1.1: Optimal Placement of Relays and BSs in OffNetworks 100 % Completed

Task 1.2: Mobile Relays and Mobile BSs Trajectory 

Optimization and Scheduling 100 % Completed

Task 1.3: Network Resilience: FRs aided by a Mobile BS or 

Relay 100 % Completed

Task 2.1: Base Station QPP eMBMS

100 % Completed;

Further Investigation in Year 2, in 

collaboration with NIST/CTL

Task 2.2: Base Station to Off-Network UE Multicast 80 % Completed;

In Progress, TBC by Year 2, Q10

Task 2.3: UE-to-UE Multicast 100 % Completed;

Extended Results with Task 2.2
Project Overview

Accomplishments - Productions

7

Ph.D. Dissertations Completed

• Mahir Ayhan, “Efficient Resource Allocation Algorithms for 4G-LTE Networks,” May 2018.

• Chen Shen, “Enhancing Wireless Network Performance and Security through Physical Layer Properties,” May 2019.

• Siyuan Feng, “On Enhancing Communications among End-Users with Limited Network Accessibility,” in progress.

Papers Published

• M. Ayhan, & H.-A. Choi, “A Priority and QoS-Aware Scheduler for LTE Based Public Safety Networks,” in Future of 

Information and Communication Conference (FICC) 2019, San Francisco, CA, March 2019.

• C. Shen, M. Yun, A. Arora, & H.-A. Choi, “Efficient Mobile Base Station Placement for First Responders in Public Safety 

Networks,” in FICC 2019, San Francisco, CA, March 2019.

• C. Shen, M. Yun, A. Arora, & H.-A. Choi, “Dynamic Placement Algorithm for Multiple Classes of Mobile Base Stations in Public 

Safety Networks,” in 2019 EAI International Conference on Cognitive Radio Oriented Wireless Networks (CROWNCOM), 

Poznan, Poland, June 2019.

•

‡

C. Liu, C. Shen, J. Chuang, R. A. Rouil, & H.-A. Choi, “Throughput Analysis between Unicast and MBSFN from Link Level to 

System Level,” in 2019 IEEE Vehicular Technology Conference (VTC-Fall), Honolulu, HI, September, 2019.

[‡]: In Collaboration with NIST/CTL Researchers
Project Overview

Accomplishments - Productions

8

Paper Under Review

•

‡

S. Feng, H.-A. Choi, D. W. Griffith, & R. A. Rouil, “On Selecting Channel Parameters for Public Safety Network 

Applications in LTE Direct,” under review.

Papers In Progress

•

‡

S. Feng, C. Shen, C. Liu, R. A. Rouil, & H.-A. Choi, “Optimal Resource Allocation for LTE eMBMS in PriorityEnabled Public Safety Networks,” in progress.

• S. Feng, & H.-A. Choi, “Multi-hop Direct Communications: from Base Station to Out-of-Coverage UEs through 

D2D-enabled UE Relay,” in progress.

•

‡

C. Liu, C. Shen, J. Chuang, R. A. Rouil, & H.-A. Choi, “Evaluating Unicast and MBSFN in Public Safety 

Networks,” in progress.

•

‡

S. Feng, H.-A. Choi, C. Liu, & R. A. Rouil, “Performance Analyses of V2X Direct Communications in Public 

Safety under 4G LTE and Future 5G NR,” in progress.

[‡]: In Collaboration with NIST/CTL Researchers
Task 2: LTE Multicast for Public Safety Networks 

9

• Task 2.1: Base Station QPP eMBMS

• Optimal Resource Allocation for eMBMS

• Performance Analysis of Unicast/Multicast 

(in collaboration with NIST/CTL)

• Task 2.2: Base Station to Off-Network 

UE Multicast

• Multi-hop D2D Communications; 

(continuation from results of Task 2.3)

• Task 2.3: UE-to-UE Multicast

• On Selecting Channel Parameters to 

provide QoS for Public Safety Network 

Applications in LTE Direct
Task 2.3 UE-to-UE Multicast: 

Channel Parameters of D2D Communication

• Mode 2 Communication of the ProSe (Proximity Services) under LTE Direct

• D2D communications in Out-of-Coverage areas through Sidelink (SL) 

channels

• SL Parameters related to Time-Domain, as well as to Frequency-Domain

and Channel Quality

• User Equipment (UEs) allocate resources autonomously without 

backhauling

• Fixed Uniform Parameter Settings base on 3GPP Specification [TS 

36.101]

• However, when the number of simultaneous transmissions (broadcasting) 

increases, the Transmission Reliability, i.e. the likelihood of successful 

receptions, degrades rapidly. 

• Since there’s no centralized coordination, nor ‘effective’ mechanisms 

to mitigate packet loss‡due to competing for resources. 10

Table A.6.5-1/2 for 10 MHz BW

Ref. Ch. Allocated 

RBs

MCS 

Index

TB Size

CD.1 10 5 872

CD.2 10 14 2 536

CD.4 50 14 12 960

CD.5 2 10 328

CD.7 50 23 25 456

[‡]: D. W. Griffith, F. J. Cintron, and R. A. Rouil, “Physical sidelink control channel (pscch) in mode 2: 

Performance analysis,” in 2017 IEEE International Conference on Communications (ICC), May 2017, pp. 

17

Channel Parameters of D2D Communication

Statement of Problem

• For OOC half-duplex UEs, all within others’ proximities, and all transmitting their 

own data simultaneously

• We focus on a UE of interest (UE-1), who currently has an MCC message with a 

minimum throughput requirement to disseminate within the group of FRs.

• What is the most efficient approach to fulfill this transmission requirement for 

UE-1, such that as many other UEs as possible (Coverage) can successfully receive 

the message within the range of some acceptable amount of delay (Capacity)?

• Our problem is different from those in related work to better fit PSN scenarios:

• We focus on a specific MCC UE of interest, instead on a random UE and aggregate 

throughput.

• We focus on the Guaranteed Bit Rate to ensure MCC delivery, instead of aggregated 

throughput of the Best Effort.

12
Channel Parameters of D2D Communication

Results and Impact

• We conduct Monte Carlo simulations, and produced trustworthy results of the 

Channel Reliabilities of the PSCCH and PSSCH under various system parameter 

settings and group sizes.

• With the Data Channel Reliability results and different throughput requirement 

values, we derive the corresponding amount of delays under various system 

parameter settings and group sizes.

• For the amount of delay results, we generated sets of tables with completeness; 

hence network operators can setup the channel/RP and UEs accordingly based on 

delay budget, as well as drafting new delay policies based on our results.

• The parameters can be pre-configured, or be dynamically adopted on the fly.

13
• Multi-hop Direct Communications

• Base Station ⟹ On-Network D2D-enabled UE ⟹ Off-Network D2D-enabled UEs

• Downlink Unicast through Uu link

• Sidelink Broadcast (through PC5 link) in Direct Mode 4 (or 3)

• Utilizes Sidelink methodologies and results from Task 2.3, along with Sidelink Relay

specifications from the 3GPP Proximity Services (ProSe) Protocols

• S. Feng, & H.-A. Choi, “Multi-hop Direct Communications: from Base Station to Out-of-Coverage 

UEs through D2D-enabled UE Relay,” in progress.

14

Task 2.2 Base Station to Off-Network UE 

Multicast
15

Further Discussions

• V2X as an Extension/Upgrade to D2D

• Stalling of D2D in industrial development; lacking of chipset manufacturers

• Rapid development and active promotion of V2X; more clear and concrete use cases 

with more vital impacts: ambulance emits signals to vehicles not in Line-of-Sight, and

communications via local infrastructures, for instance light poles, and etc.

• Potentially higher Reliability and less Delay ⇒ higher Coverage and Capacity

• V2X in 5G NR

• Much more Flexible Frame Structure allowing shorter scheduling period ⇒

faster channel measurement and adaptation, less delay, which is primary for V2X.

• Allowing higher moving speed FR devices and mobile infrastructures to maintain 

communication reliability.
16

Task 1: Intelligent, Dynamic Coverage, Capacity, 

and Resilience Enhancement

• Task 1.1: Optimal Placement of Relays 

and BSs in Off-Networks

• Task 1.2: Mobile Relays and Mobile BSs 

Trajectory Optimization and Scheduling

• Task 1.3: Network Resilience: FRs aided 

by a Mobile BS or Relay
Intelligent Mobile Base Station Placement

Introduction

17

• What is Mobile Base Station (mBS)?

• Mobile BS is not frequently being used in our normal life. Exceptions can 

be the big events like live music shows or popular sports games which are 

predictable and prepared, or the disasters which usually can not be 

predicted.

• The usage of mobile BS in disastrous situations (i.e. in PSNs) is our 

interest. The following facts need to be always considered while solving 

the problem of providing wireless communications for PSN.
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• mBS should be “strong” enough to survive the disaster, which usually 

makes it sacrifice some performance and convenience.

• Quick deployment, setting up the communication quickly can be 

significant for First Responders (FR).

• Performance as good as possible, like serving more FRs, providing more 

bandwidth, and always put reliability with high priority.

• Good algorithms to initialize/deploy them. There is no such problem for 

constant BS. Since the mobility of mBS, the optimal way to initialize them 

or even dynamically running them is a new problem in its field.

Intelligent Mobile Base Station Placement

Specialties of mBS in PSN
19

Intelligent Mobile Base Station Placement

BS Comparisons - per FirstNet.gov - 2016

Characteristic

VNS

(Vehicle 

Network 

System)

CoLT

(Cells on 

Light Truck)

CoW

(Cells on 

Wheels)

SoW

(System on 

Wheels)

DACA

(Deployable Aerial 

Communications 

Architecture) 

Capacity Low Medium High High Low

Coverage Low High High High High

Band 14 Radio Yes Yes Yes Yes Yes

Standalone Yes No No Yes No

Availability Immediately Drive Time Drive Time Drive Time Launch Time

Power Vehicle 

Batteries Generator Generator Generator Airframe
20

Characteristic

VNS

(Vehicle 

Network 

System)

CoLT

(Cells on 

Light Truck)

CoW

(Cells on 

Wheels)

SoW

(System on 

Wheels)

DACA

(Deployable Aerial 

Communications 

Architecture) 

Deployment 

Time Zero Medium Medium Long Long

Incident 

Duration Low Medium Medium Long Long

Physical 

Nature FR Vehicle Dedicated 

Truck

Dedicated 

Trailer

Dedicated 

Trailer Aerial

Typical 

Number Thousands Hundreds Hundreds Dozens Not Yet Used

Intelligent Mobile Base Station Placement

BS Comparisons - per FirstNet.gov - 2016
21

Intelligent Mobile Base Station Placement

UE (FR) Mobility Models

1. Random Destination Point Oriented

2. Random Direction with Bound Oriented

3. Leader Direction Oriented, with Leaders using Random Direction with 

Bound

4. (Point of Interest) POI Location Oriented

• UE moves very fast in the demo for better visual effect.

• Pause behaviors may be introduced later.
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1. Random Destination Point Oriented

UE randomly selects a destination and 

then move towards it in a straight 

line.

After reaching the destination, 

another one is randomly selected.

• Trucks: System on Wheels (SoW)

• Automobiles: Cells on Wheels 

(CoW)

• Drones: Deployable Aerial 

Communications Architecture 

(DACA)
23

2. Random Direction with Bound Oriented

UE randomly selects a direction 

within a range based on its current 

direction, and moves a single step.

Then, another direction is randomly 

selected and moves again.
24

3. Leader Direction Oriented, with

Leaders using Random Direction with Bound

The blue dots represent group 

leaders, who have higher priorities 

than the black dots, i.e. regular group 

members.
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4. (Point of Interest) POI Location Oriented

• Fire Disaster Locations: Point of 

Interest Locations
26

Intelligent Mobile Base Station Placement

Related Work

• For mobile base station deployment (PSN oriented or not), there are 

many works in this field. Most of them focus on drone based mBS only.

• The problem modeling is also very diverse. Some assume drone always 

moves with a constant speed. Some assume drone with MIMO capability 

and that its configuration can be optimized dynamically, etc.

• Algorithm can be brute force (location problem can be NP-hard) or game 

theory based (no coordination.)
27

Dynamic Heterogeneous mBS Deployment 

Algorithm

• The mBS are deployed with centralized control.

• Different types of mBS have been researched and 3 are modeled in our 

simulation.

• Multiple UE mobility models have implementation for dynamic 

deployment performance analysis.
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Intelligent Mobile Base Station Placement

System Model and Performance Metric

mBS Class Drone CoW SoW

Capacity Weights 1 1.5 2

Recalculation Period

(multiples of 10 seconds) 1 30 90

Relocation Threshold (meters) 1 30 50

Height (meters) 30 10 10

Transmit Power (watts) 20 30 40
29

Intelligent Mobile Base Station Placement

Results of Placement Algorithms

Blue and black dots represent UEs, where blue ones are leaders in leading UE mobility 

model. Drones, CoW and SoW are presented as Red Triangles, Magenta Squares, and 

Green diamonds, respectively.
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Intelligent Mobile Base Station Placement

Algorithm 1
31

Intelligent Mobile Base Station Placement

Flowchart of Periodical Recalculation of mBS
32

Intelligent Mobile Base Station Placement

Empirical Result

• The results are analyzed with four mobility models and four deployments

• Deployment 1 – 5 Drones, 2 CoWs, 1 SoW (Heterogeneous)

• Deployment 2 – 10 Drones Only

• Deployment 3 – 5 Cells on Wheels Only

• Deployment 4 – 3 System on Wheels Only

• The baseline algorithm used as comparison uses the same set of mBS with 

stationary and regular placement.
33

Intelligent Mobile Base Station Placement

Comparisons with Baseline Method

• Random Walk V2

• Deployment 1:

• 5 Drones, 2 CoWs, 1 SoW
34

Intelligent Mobile Base Station Placement

Comparisons with Baseline Method

• Follow Leader

• Deployment 1:

• 5 Drones, 2 CoWs, 1 SoW
35

Intelligent Mobile Base Station Placement

UE SINR

Deployment 1 Deployment 2 Deployment 3 Deployment 4

0

5

10

15

UE average SINR in dB

Random Walk V1

Random Walk V2

Follow Leader

POI

High capacity mBS classes, 

such as CoW and SoW, with 

POI model dominate the SINR 

performance.
36

Intelligent Mobile Base Station Placement

UE 5th Percentile SINR - at Cell Edge

Deployment 1 Deployment 2 Deployment 3 Deployment 4

-15

-10

-5

0

5

10

Average UE 5th percentile SINR in dB

Random Walk V1

Random Walk V2

Follow Leader

POI

Similar conclusion can be drawn 

for the POI model. But due to the 

high flexibility of drones, 

Deployment 2 with drones only 

outperforms the mixed 

scenarios of Deployment 1.
37

Intelligent Mobile Base Station Placement

UE to mBS Distance

Deployment 1 Deployment 2 Deployment 3 Deployment 4

0

50

100

150

200

250

Average distance bewtween UE and mBS in meter

Random Walk V1

Random Walk V2

Follow Leader

POI

The drone mBS with better 

moving flexibility 

outperformances others in the 

distance metrics.
38

Further Discussions

• In the reality, each kind of mBS should be associated with corresponding cost in 

operation, which is not considered in our current work. 

• In that case, the optimal deployment should depend on the “budget” or the mBS' 

availability in each kind, and the disaster’s property.

• The disaster scenario and the communication requirements from FRs can vary drastically 

over time, which is not considered in our current work. 

• For example, in the POI model, the point of interest can move due to the disaster’s 

changing or other factors.

• The simulation result could provide a sight of basic understanding on various 

deployments and disaster situation.
39

Further Discussions

• Utilizing V2X Communications in mBS Placement

• Vehicular systems (V) as the mBS, and the FR UEs as the Device (X)

• Autonomous mBS Placement through both V2D and V2V direct communications

• Off-Load Network, as well as enabling Off-Network Deployment, by coordinating 

surrounding UEs’ communications
Project Summary In Progress

During the past two years, we have achieved the 

following goals of this project:

• Delivered several related technical reports, 

and publications completed (some in 

collaboration with NIST/CTL researchers);

• Achieved most of the proposed tasks with 

their corresponding milestones and 

deliverables. Overall, we have completed 

about 97% of the proposed research of this 

project.

During the remaining project period, we will be 

focusing on finishing the following items:

• Completion of Task 2.2: “Multi-hop Direct 

Communications: from Base Station to 

Out-of-Coverage UEs through D2D-enabled 

UE Relay” (based on results of Task 2.3);

• Completion of more publications;

• Completion of the final report.

Conclusion

40
Q & A

41
THANK YOU
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Session

2:25 PM

43

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