DRAFT 

DRAFT CBRS Ecosystem Sharing Assessment (SEA) 

Test and Metrology Test Plan:

Characterize aggregate emissions within the CBRS band 3550-3700

MHz in at least one Always-On DPA 

5 Duncan A. McGillivray, Todd Schumann, Thao T. Nguyen 

Douglas Boulware, M. Keith Forsyth 

Dazhen Gu, C. Lee Joyce 

Elyssa Kaplan, Mark Krangle 

Daniel G. Kuester, Joseph R. Mruk 

10 Anthony W. Romaniello, Aric W. Sanders 

Robert Scheeler, Stephen Segro 

NASCTN Program Manager: Melissa M. Midzor 

NASCTN Project Manager: Keith A. Hartley 

November 1, 2023 

15 
DRAFT 

DISCLAIMER 

Certain commercial entities, equipment, or materials may be identifed in this document in 

order to describe an experimental procedure or concept adequately. Such identifcation does not 

imply any recommendation or endorsement by National Institute of Standards and Technology 

(NIST) or National Advanced Spectrum and Communications Test Network (NASCTN), nor is 

it intended to imply that the materials or equipment identifed are necessarily the best available 

for the purpose. 

This document is available free of charge from: 

https://www.nist.gov/programs-projects/cbrs-sharing-ecosystem-assessment 
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Preface 

The National Advanced Spectrum and Communications Test Network (NASCTN) provides an impartial, scientifcally rigorous forum for addressing spectrum-sharing challenges in an effort to accelerate the deployment of wireless 

technologies among commercial and federal users and to measure the impacts of spectrum dependent systems deployments. 

NASCTN provides robust test processes, validated measurements data, and statistical analysis necessary to develop, 

evaluate, and deploy spectrum sharing technologies that can improve access to the spectrum by both federal agencies 

and non-federal spectrum users. 

Representatives from Defense Information Systems Agency (DISA) Defense Spectrum Organization (DSO) submitted 

a proposal to NASCTN to measure emissions in the Citizens Broadband Radio Service (CBRS) band to collect data 

required for DISA DSO to ascertain the effectiveness of the sharing ecosystem between CBRS systems as managed 

by Spectrum Access Systems (SASs), and Department of Defense (DoD) systems as monitored by Environmental 

Sensing Capabilitys (ESCs). The collect data will be used by DoD to provide insight into the sharing ecosystem’s 

effectiveness, and track changes in the spectrum environment over time. 

After the NASCTN Steering Committee accepted the DSO proposal as a NASCTN test, NASCTN assembled a test 

plan development team comprised of experienced engineers and other professionals. The results of that work plus the 

knowledge and creativity of the team members culminated in the production of this test plan. 

The test plan is designed to yield reproducible measurements. This NASCTN effort focuses on the viability of spectrum sharing in the CBRS band in Always-On Dynamic Protection Areas (DPAs). NASCTN will solicit comments on 

this test plan from CBRS federal and non-federal stakeholder community. 

A description of NASCTN and listing of Charter Members at the time of publication of this report follows. 
DRAFT 

National Advanced Spectrum and Communications Test Network (NASCTN) 

The mission of the NASCTN is to provide, through its members, a network for robust test processes and validated 

measurement data necessary to develop, evaluate and deploy spectrum sharing technologies that can improve access 

to the spectrum by both federal agencies and non-federal spectrum users. 

45 NASCTN is a member organization under a charter agreement. Members 

• Facilitate and coordinate work with federal, academic, and industry spectrum users to rapidly and cooperatively 

facilitate spectrum sharing and co-existence studies; 

• Work as a partnership to address the interests and equities of all spectrum stakeholders in a fair, equitable, and 

non-preferential manner; and 

50 • Through sharing of technical resources, with consideration for cost, provide liaison and support to coordinate and 

leverage existing national capabilities supporting government, academic, and industry testing and evaluation known 

to improve and expedite spectrum sharing and coexistence. 

Charter members at the time of publication of this report are (in alphabetical order): 

• Department of Defense Chief Information Offcer (DoD CIO) 

55 • National Aeronautics and Space Administration (NASA) 

• National Institute of Standards and Technology (NIST) 

• National Oceanic and Atmospheric Administration (NOAA) 

• National Science Foundation (NSF) 

• National Telecommunications and Information Administration (NTIA) 

60 National Institute of Standards and Technology (NIST) hosts the NASCTN capability at the Department of Commerce 

Boulder Laboratories in Boulder, Colorado. 
DRAFT 

Technical Contributors 

Contributor Organization Primary Contribution Areas 

Jordan L. Bernhardt 

M. Keith Forsyth 

Duncan A. McGillivray 

Aric W. Sanders 

Stephen Segro 

Dazhen Gu 

Daniel G. Kuester 

John M. Ladbury 

Thao T. Nguyen 

Robert Ballard 

Douglas Boulware 

Joel Dumke 

Bradley Eales 

Justin Haze 

Peter Mathys 

Anthony W. Romaniello 

Todd Schumann 

C. Lee Joyce 

Keith Hartley 

Mark Krangle 

Elyssa Kaplan 

Joseph R. Mruk 

Robert Scheeler 

NIST NASCTN 

NIST NASCTN 

NIST NASCTN 

NIST NASCTN 

NIST NASCTN 

NIST CTL 

NIST CTL 

NIST CTL 

NIST CTL 

NTIA ITS 

NTIA ITS 

NTIA ITS 

NTIA ITS 

NTIA ITS 

NTIA ITS 

NTIA ITS 

NTIA ITS 

NASA LaRC / AMA 

MITRE 

MITRE 

MITRE 

MITRE 

MITRE 

Data edge compute and analysis 

Sensor system, and felding 

Project technical leadership 

System verifcation, bench acceptance testing, and calibration 

Sensor system co-lead, site survey, and felding 

System verifcation, bench acceptance testing and calibration 

Date edge compute and analysis 

Consultation to sensor system and calibration 

Project technical leadership, modeling and simulation 

SCOS software and implementation 

SCOS software and implementation 

Consultation to modeling and simulation 

SCOS software and implementation 

SCOS software and implementation 

Date edge compute and analysis 

Integrated product, site survey, and sensor felding 

Project technical leadership, Integrated product 

Site survey, and sensor felding 

Project management 

Sensor system, and sensor felding 

Sensor system 

Sensor system 

Sensor system, and modeling and simulation 

v 
DRAFT 

Acknowledgments 

65 We would like to thank the many additional individuals and organizations that made valuable contributions over the 

course of this test plan development phase. 

The insights gathered from the test plan sponsor Defense Information Systems Agency (DISA) Defense Spectrum 

Organization (DSO), federal, and industry representatives of the Interagency-Joint Working Group on Citizens Broadband Radio Service (CBRS) were crucial to the framing of the development effort. We thank industry for hosting 

70 NASCTN at Wireless Innovation Forum (WInnForum), On-Go Alliance, and the On-Go Alliance board members 

meeting. 

In addition, we thank Rebecca Dorch of National Telecommunications and Information Administration (NTIA) Institute 

for Telecommunication Sciences (ITS) for providing crucial access, context, support and insights. 

We thank United States Marine Corps (USMC) representatives Seth Nagy, and Chief Warrant Offcer Figueroa for 

75 their coordination and site survey efforts at USMC Camp Pendleton, which were formative to this test plan. 

The initial National Advanced Spectrum and Communications Test Network (NASCTN) Sharing Ecosystem Assessment (SEA) sensor demonstration would not have been possible without the explicit support of the 3.5 GHz Transition Plan through Department of Defense (DoD) Chief Information Offcer (CIO), DISA DSO, and Department of 

Navy (DoN) CIO. We recognize the consultation and technical engagement by Naval Sea Warfare Center Dahlgren 

80 Division’s Stacy Barker, Andrew Simpson, Han Lee, and Mike Mearns. 

For the hosting, coordination, and deployment of NASCTN SEA sensors we thank Professor Bill Moore of Hampton 

University, Kenneth Dudley of the Longbow Group, and Randy Plotkin of the Port of Virginia. 

The technical team appreciates the opportunity and continued encouragement to participate in this project and recognizes their leadership: NASA LaRC Branch head Jay Ely, and Test Range Manager Charmaine Franck 

85 NIST CTL Division Chiefs Paul Hale, Melissa Midzor, Richard Rouil 

NTIA ITS Division Chiefs Michael Cotton, Julie Kub, and Charles Dietlein 

Also, the technical team thanks the NASCTN program manager, Melissa Midzor, the NASCTN administrative offcer 

Amanda Hyman, division offce manager Julie Zumhofe, and former NASCTN project manager Matthew Briel for 

90 their efforts. 

Finally, thanks to Kasey Pugh of DISA DSO and supporting staff led by Tim Mull of The MITRE Corporation 

(MITRE) for their sponsorship, continued guidance and feedback throughout the evolution of the SEA project. 

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Executive Summary 

The purpose of this National Advanced Spectrum and Communications Test Network (NASCTN) project is to provide 

data-driven insight into the Citizens Broadband Radio Service (CBRS) sharing ecosystem’s effectiveness between 

commercial and Department of Defense (DoD) incumbent systems, and to track changes in the spectrum environment 

over time. CBRS is the “frst of a kind” nationwide shared spectrum ecosystem in the 3550-3700 MHz band. It 

includes a 3 tier approach (Incumbent, Priority Access License (PAL), General Authorized Access (GAA)), specifc 

sharing stipulations as notated in the Federal Communications Commission (FCC) Part 96 rules, and Environmental 

Sensing Capability (ESC) which detects incumbents and Spectrum Access System (SAS) that coordinate spectrum 

access. This project is part of the DoD 3.5 GHz Transition Plans that focus on ecosystem validations, environmental 

assessments, and continued engagement on refning the CBRS infrastructure. 

The FCC established the CBRS in the 3550-3700 MHz band, and created a frst of its kind three-tiered access and 

authorization framework to accommodate Federal incumbent and non-federal users which are tiered as PAL and GAA 

users. The FCC auctioned the band (Auction #105) for commercial use in July 2020. A system has been established to 

enable this sharing, which is composed of two main parts: SASs which perform automated spectrum use coordination, 

and ESCs that detect incumbent use of the band and transmit that information back to the SASs. 

This project is divided into four major components: 

• 1 - Passive Observation: Characterize aggregate emissions within the CBRS band 3550-3700 MHz with and without 

Dynamic Protection Area (DPA) activations, in at least two coastal DPAs. Assess ecosystem performance to timely 

respond, and measure increase in noise foor due to CBRS deployments over time. 

• 2 - Passive Observation: Characterize aggregate emissions within the CBRS band 3550-3700 MHz in at least one 

Always-On DPA. Assess ecosystem performance to limit CBRS emissions in the Always-On DPA. 

• 3 - Active Experimentation: Evaluate ESC detection performance in the Field (with Navy Ship). For a representative 

set of ESCs in the feld, assess true-positive and false-positive rates and independently verify incident power levels 

at ESC sensor locations. 

• 4 - Long Term support - Collection and analysis of Passive Observations over 4 years, and support transfer of data 

to a DoD data repository. 

This test plan addresses Passive Observation in the Camp Pendleton Always-On DPA. Separate test plans will be 

developed for Passive Observation in Coastal DPAs and Active Experimentation. 

Due to CBRS-related research, testing, and analysis conducted by NASCTN and technical discussions with the sponsor 

since the creation of the frst iteration of this test plan, the utility of the Passive Observations in the Camp Pendleton 

Always-On DPA has been reduced. In light of this, NASCTN will continue efforts to collect data but reserves the right 

to signifcant reduce or alter the test plan, in coordination with the project Sponsor, to apply more resources to other 

tasks in the CBRS Sharing Ecosystem Assessment (SEA) project. 

0.1 Overview of Baseline Measurements and Analysis 

The sensor data from locations within US Marine Corps Camp Pendleton will serve as localized insights on the 

presence, level, and statistical distribution of radio frequency (RF) emissions in the CBRS band (3550 – 3700) MHz 

observable from within Camp Pendleton. The sensor data is channelized to 10 MHz bands and aligns with CBRS 

channel protection bandwidths. In addition, optional sensor collections at 3450 – 3550 MHz and 3700 – 3710 MHz 

can be carried out at an increased uncertainty to provide data in the transition regions between adjacent bands with 

respect to CBRS. 

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The individual sensor’s edge compute data products outlined in Chapter 6 of this test plan will be processed and validated for integrity through automated means upon insertion into NASCTNs central repository. Additional validation, 

inspection, and in-depth analysis of data will be performed by NASCTN and are anticipated to converge towards a 

monthly cadence and published in conformance to the publication guidelines outlined in the Data Sharing Plan. 

Sensor improvements: Each sensor node within the Camp Pendleton complex will be initially deployed and confgured 

to collect data unsynchronized with respect to other sensors. Time synchronization between sensors through Network 

Time Protocol (NTP) may be of interest and can be added without hardware upgrades post deployment. Synchronization may be warranted in the event time-alignment with external data sources proves to be too onerous in post analysis 

techniques or additional value is ascribed in the cross correlation amongst sensors. 

0.1.1 Anticipated Analysis Insights 

1. radio frequency (RF) frequency and RF time domain analysis of localized spectrum activity evaluated at macroscales 

relative to 3rd-generation partnership project (3GPP) time intervals (minutes, hours, days, weeks). The analysis 

will be focused on the aggregation of the following channelized edge compute data products: RF power vs time 

in dBm / 10 MHz, power spectral density (PSD) in dBm / Hz, amplitude probability distributions in quantiles vs 

dBm / 10 MHz, and summary statistics of periodic frame power (PFP) in dBm / 10 MHz. 

(a) Channel based analysis of received RF power with quantifed uncertainties. Analyses will take into account 

individual sensor’s edge compute data readings, such as the RF power within the 10 MHz evaluation channels 

and ascribe statistical properties to ascertain distributions in RF power and its variability over time. 

(b) Understand the propensity for carrier confgurations in allocation bandwidth and time division duplex (TDD) 

morphology as carriers continue to build out networks. Here TDD morphologies may need to be analyzed at 

fner timescales as are available in PFP edge compute data sets. 

(c) Spectrum activity with respect to RF power measurements on human life cycle time intervals (diurnal, weekday/weekend, long term growth trends). Here we note a potential for a bifurcation of RF power due to power 

level restrictions within a neighborhood distance of the base in the 3550-3650 MHz portion of the CBRS band. 

2. An analysis of 3GPP TDD time structure as observed through periodic frame power edge compute products that 

records channelized power vs time statistics on a 3GPP frame basis of 10 milliseconds. An analysis will be heavily 

based on evaluations of channelized PFP measurements. 

(a) Data to aid in the refnement of RF emissions models to be used in estimating RF emissions at points of interest. 

Where notably, RF emissions models do not incorporate time dependent dynamics in TDD structures; and a 

statistical representation of short duration peak vs average power statistics could be of interest to the community 

focused on understanding impact responses to receiver architectures in the CBRS band. 

(b) Identifcation of synchronization and de-synchronization effects across disparate CBRS networks as PAL and 

GAA deployments continue to densify. Understanding the degree to which synchronization of TDD signal 

structures across networks is presented in-situ are insightful to susceptibility test designers seeking to derive 

representative waveforms and ranges of conditions for coexistence tests. 

3. DoD incumbent operations within nearby e-DPA impacts on commercial coastal spectrum availability. The NASCTN 

sensors deployed within Camp Pendleton, are anticipated to serve as part of the chain of sensors dedicated to the 

evaluation of spectrum dynamics due to incumbent operations. It is believed to be intractable to separate the effects 

from nearby DPAs from sensor measurements within Camp Pendleton. 

0.1.2 Potential Correlation Actions 

Correlations with full-activity-dump data: 

1. Analysis of allocation sizes 

2. Aggregate interference check calculations and measured RF power 

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0.1.3 Uncertainties and limitations on the extension of measured RF emissions to adherence 

assessments to the Camp Pendleton Always-on Protection criteria 

For Camp Pendleton, the always-on protection criteria specify reference antenna patterns, channelized protection 

limits in peak RF power (dBm/10MHz), and channelized neighborhood distances (km). 

The aggregate interference is being managed by acp! (acp!)SAS such that only the Citizens Broadband Radio Service 

devices (CBSDs) within the neighborhood distance sum to the allowable RF power outlined in the protection criteria. 

In contrast, a NASCTN sensor will measure the RF emissions from all CBSDs within its RF horizon and is unable 

to distinguish energy contributions from individual sources of emissions. Understanding energy contributions from 

individual sources is beyond the capabilities of the current approach. 

To date, modeling using obfuscated full-activity-dump data suggests, a deviation margin between RF sensor collects 

and protection thresholds traced to RF antenna gain and local RF geometry. While it is currently believed that the 

sensor collect extensibility to assessment of a protection criteria is not tractable, the measurements could provide for 

an upper bound of the expected emissions. 

0.2 NASCTN Process Activities 

NASCTN projects follow a 5 stage open, transparent, comprehensive process for developing and executing independent, scientifcally based test plans, validating test results, and reporting fndings. This test plan is part of Stage 

III, which includes conducting investigations, building prototypes/test beds, design of experiment, and pilot studies. 

During this stage, the team completed three major efforts to inform the development of this test plan: 

• Sensor site selections - Site surveys for multiple locations within several DPAs were conducted. One Always-On 

DPA at Camp Pendleton was chosen for this test plan. 

• Modeling - Incorporated existing methods and industry provided data to better inform site selections, and to identify 

measurements to achieve project goals. 

• Prototype SEA sensor/ Edge Data collection systems - Provide insights into CBRS ecosystem behavior at representative locations; exercise remote sensing nodes which will perform time autonomous, calibrated measurements with 

quantifed levels of uncertainty; and trial edge-compute data products in situ. 

In addition, extensive outreach to the CBRS community was undertaken in order to aid in resolving underlying assumptions of the test framework, gain improvements and insights to arrive at a tractable and scoped test plan, and 

context to broaden utility of the NASCTN test and data collection to beneft the CBRS community. 

The general approach to achieve the objectives of this test plan consists of deploying a quantity of custom-build 

emission collection sensors and analysis software designed to detect, observe, and collection CBRS emissions. A 

number of sensors will be strategically located within the Camp Pendleton Always-On DPA to continuously monitor 

the CBRS channels for any emissions that exceed the threshold for Always-On DPA. The collection system and 

software collects basic occupancy data such as maximum, minimum, mean, and median power collected by repeatedly 

scanning each of the CBRS 10 MHz channels. The emissions are analyzed onsite and collected data is transmitted 

back to NASCTN for storage and further analysis. 

The deliverables planned for this test effort include: 

• A test report which includes a repeatable test methodology to collect CBRS ecosystem data to inform on the emission levels within the CBRS band for Always-On DPAs. 

• A set of validated data with uncertainties from feld collections and modeling to augment the interpretation of feld 

collects. 

• An initial set of analysis to interpret the collected data 

• A yearly report summarizing the data collected over the preceding year 

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• Support establishing a repository for stakeholder access to collected, processed, and analyzed data 

NASCTN solicited and adjudicated comments on this test plan from the CBRS stakeholders within the Federal and 

220 non-federal communities which are refected in this document. Community outreach efforts, such as this test plan 

document and adjudication, are utilized to gain insights and refne detailed test plans before they are fnalized. 

The technical approach and deliverables are anticipated to be refned over time so as to evolve with the changes in 

the ecosystem in conjunction with needs of stakeholders. Major deviations, though not anticipated at this time, from 

the outset of the project will be communicated to the sponsor and documented in detail in NASCTN annual technical 

225 reports. 

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Contents 

Technical Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v 

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi 

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii 

0.1 Overview of Baseline Measurements and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . vii 

0.1.1 Anticipated Analysis Insights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii 

0.1.2 Potential Correlation Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii 

0.1.3 Uncertainties and limitations on the extension of measured RF emissions to adherence assessments to the Camp Pendleton Always-on Protection criteria . . . . . . . . . . . . . . . . . . ix 

0.2 NASCTN Process Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 

List of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv 

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviii 

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix 

1 Introduction 1 

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

1.1.1 NASCTN Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

1.1.2 CBRS Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 

1.1.3 Test Request . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 

1.2 Test Methodology Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 

1.2.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 

1.2.2 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 

1.2.3 Phase 1 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 

1.2.4 Next steps towards Camp Pendleton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 

1.2.5 Phase 2 Anticipated Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 

1.2.6 Deliverables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 

2 Modeling and Simulation 6 

2.1 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 

2.1.1 Obfuscated Data on CBRS Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 

2.1.2 Propagation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 

2.1.3 WInnForum Reference DPA Move List Algorithm . . . . . . . . . . . . . . . . . . . . . . . 8 

2.2 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 

2.2.1 Examples of Task 2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 

3 Modeling Informed Site Selection 10 

3.1 Methodology Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

3.1.1 Applied Aggregate Emission Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

3.1.2 Example of Aggregate Emission Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . 11 

3.1.2.1 Randomized CBSD Deployment and Sensor Location . . . . . . . . . . . . . . . . 11 

3.1.2.2 Measured Power vs. Estimated Aggregated Emissions Comparison . . . . . . . . . 11 

3.1.2.3 Monte Carlo Simulation of CBSD Deployments . . . . . . . . . . . . . . . . . . . 14 

3.2 SEA Sensor Siting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 

3.2.1 Site Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 

3.2.2 Preliminary Aggregate Emission Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 

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3.2.3 Key Technical Challenges with Current Simulation and Modeling . . . . . . . . . . . . . . . 16 

3.3 Informing Data Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 

4 Sensor System 19 

4.1 Sensor Spiral Development Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 

4.2 Sensor Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 

4.2.1 RF System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 

4.2.1.1 Antenna (ANT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 

4.2.1.2 Bandpass Filter (BPF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 

4.2.1.3 Low Noise Amplifer (AMP1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 

4.2.1.4 Realtime Spectrum Analyzer (RSA) . . . . . . . . . . . . . . . . . . . . . . . . . 23 

4.2.1.5 Embedded Computer (CMP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 

4.2.2 Cascade RF Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 

4.2.3 On-board Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 

4.3 Sensor Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 

4.3.1 Preselector Assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 

4.3.1.1 Preselector Thermal Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 

4.3.2 SPU Assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 

4.3.3 System Power Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 

4.3.4 LTE Router . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 

4.3.5 Future SPU Enhancements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 

4.4 Full-System RF Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 

4.4.1 Gain and Frequency Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 

4.4.2 Noise Figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 

4.4.3 Gain compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 

4.4.4 Sensor Overload . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 

5 Sensor Management 47 

5.1 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 

5.1.1 Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 

5.1.2 Sensor Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 

5.1.3 Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 

5.1.4 Failure Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 

5.1.5 System Monitoring and Alerting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 

5.2 Network and Data Backhaul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 

5.3 Data Repository . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 

6 Sensor Data 54 

6.1 Edge Data Payloads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 

6.1.1 Reference Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 

6.1.2 Real-Time Waveform Streaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 

6.1.2.1 Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 

6.1.2.2 Calibration Corrections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 

6.1.2.3 Digital Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 

6.1.3 Power Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 

6.1.3.1 Time-Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 

6.1.3.2 Power Spectral Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 

6.1.3.3 Periodic Frame Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 

6.1.3.4 Amplitude Probability Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . 70 

xii 
DRAFT 

6.1.4 Metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 

315 6.1.5 Data Size and Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 

6.2 Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 

7 Calibration, Verifcation, Acceptance Tests 75 

7.1 Amplitude Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 

7.1.1 Calibration of the On-board Calibration Noise Diode . . . . . . . . . . . . . . . . . . . . . . 76 

320 7.1.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 

7.1.2.1 Calibration of Sensor Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 

7.2 Measurement Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 

7.2.1 Reported Power Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 

7.2.2 Noise Figure Estimate Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 

325 7.3 Sensor Verifcation and Acceptance Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 

7.3.1 RF Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 

7.3.2 Software Verifcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 

7.3.3 System Electrical Power and Communication Verifcation . . . . . . . . . . . . . . . . . . . 91 

8 Metrology Sensor Demonstration 92 

330 8.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 

8.2 Site Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 

8.2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 

8.2.2 Locations Visited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 

8.2.3 Survey Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 

335 8.2.4 RF Survey Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 

8.2.5 Camp Pendleton Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 

8.3 CBRS-ecosystem observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 

8.3.1 Green Mountain Mesa, Boulder, CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 

8.3.2 East 1 ESC-monitored DPA (E-DPA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 

340 9 Conclusions 99 

Bibliography 100 

xiii 
345

350

355

360

365

370

375

380

385 DRAFT 390

395

400

405

410

415

Acronyms 

3GPP 

AC 

ADC 

AGL 

AMA 

AMSL 

APD 

API 

AWGN 

CAC 

CBRS 

CBSD 

CCDF 

CIO 

COTS 

CTL 

CW 

DANL 

DFT 

DISA 

DoC 

DoD 

DoN 

DPA 

DSO 

DSP 

DTFT 

E-DPA 

EIRP 

ENR 

ENBW 

ENR 

ESC 

FAD 

FCC 

FFT 

3rd-generation partnership project 

alternating current 

analog-to-digital converter 

above ground level 

Analytical Mechanics Associates 

above mean sea level 

amplitude probability distribution 

application programming interface 

additive white Gaussian noise 

Common Access Card 

Citizens Broadband Radio Service 

Citizens Broadband Radio Service device 

complementary cumulative distribution 

function 

Chief Information Offcer 

commercial off-the-shelf 

Communications Technology Laboratory 

continuous-wave 

displayed average noise level 

discrete Fourier transform 

Defense Information Systems Agency 

Department of Commerce 

Department of Defense 

Department of Navy 

Dynamic Protection Area 

Defense Spectrum Organization 

digital signal processing 

discrete-time Fourier transform 

ESC-monitored DPA 

effective isotropic radiated power 

excess noise ratio 

equivalent noise bandwidth 

excess noise ratio 

Environmental Sensing Capability 

full activity dump 

Federal Communications Commission 

fast Fourier transform 

FIR 

GAA 

GB-DPA 

GEOID 

GMM 

GUI 

HU 

IEEE 

IIR 

IP 

IQ 

ITM 

ITS 

JSON 

KML 

LAN 

LaRC 

LNA 

LTE 

LZMA 

MITRE 

Msps 

MUF 

NASA 

NASCTN 

NIT 

ND 

NEMA 

NF 

NIST 

NPS 

NTIA 

fnite impulse-response 

General Authorized Access 

ground-based DPA 

geographic identifer 

Green Mountain Mesa 

graphical user interface 

Hampton University 

Institute of Electrical and Electronics 

Engineers 

infnite impulse-response 

internet protocol 

in-phase and quadrature 

Irregular Terrain Model 

Institute for Telecommunication Sciences 

JavaScript object notation 

keyhole markup language 

local area network 

Langley Research Center 

low noise amplifer 

long-term evolution 

Lempel-Ziv-Markov chain algorithm 

The MITRE Corporation 

mega samples per second 

microwave uncertainty framework 

National Aeronautics and Space 

Administration 

National Advanced Spectrum and 

Communications Test Network 

Norfolk International Terminal 

noise diode 

National Electrical Manufacturer’s 

Association 

noise fgure 

National Institute of Standards and 

Technology 

National Park Service 

National Telecommunications and 

Information Administration 

xiv 
DRAFT 

NTP Network Time Protocol SPDT single pole double throw 

PAL Priority Access License SPU signal processing unit 

420 P1dB 1 dB gain compression 435 SQL Structured Query Language 

PFP periodic frame power STIG Security Technical Implementation Guide 

PIV Personal Identity Verifcation SWaP size, weight and power 

PSD power spectral density TDD time division duplex 

RF radio frequency UPS uninterruptible power supply 

425 RMS root mean square 440 USFWS United States Fish and Wildlife Service 

RSA real-time spectrum analyzer USN United States Navy 

SAS Spectrum Access System USB universal serial bus 

430 

SCOS 

SEA 

SigMF 

spectrum-characterization and occu

sensing 

Sharing Ecosystem Assessment 

signal metadata format 

pancy 

445 

USMC 

VPA 

WGS-84 

WInnFor

United States Marine Corps 

Virginia Port Authority 

World Geodetic System 1984 

um Wireless Innovation Forum 

SNR signal to noise ratio 

xv 
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455

460

465

470

475

480

485

490

495

DRAFT 

List of Figures 

1.1 NASCTN’s 5 Stage Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

1.2 FCC’s three-tiered access for CBRS. Reference: https://ongoalliance.org/the-technology-behind-spectrum-sharingthe-spectrum-access-system/ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 

2.1 Geographical masks used to flter out obfuscated data for three areas of interest. . . . . . . . . . . . . . . . . . . 7 

2.1a DPA West-14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 

2.1b DPA East-1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 

2.1c Boulder and Broomfeld Counties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 

2.2 Examples of simulation results for always-on DPA Camp Pendleton. . . . . . . . . . . . . . . . . . . . . . . . . 9 

2.2a Example of neighbor list grants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 

2.2b Example of move list grants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 

2.2c Example of keep list grants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 

3.1 An example of a CBSD deployment in Boulder County and Broomfeld County, Colorado. . . . . . . . . . . . . . 11 

3.2 Sensor placement information at Green Mountain Mesa (GMM), Boulder, Colorado. . . . . . . . . . . . . . . . . 12 

3.3 An example of measured power collected by a NASCTN prototype SEA sensor at the GMM location on December 

6, 2022. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 

3.4 Measured powers collected by a NASCTN prototype SEA sensor at the GMM location from November 29, 2022 

to December 7, 2022. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 

3.5 An example of estimated aggregate emission at the GMM sensor location. . . . . . . . . . . . . . . . . . . . . . 13 

3.6 Examples of estimated aggregate emissions from 100 randomized CBSD deployments at the prototype GMM 

sensor in Boulder, Colorado. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 

3.7 Information of 2D antenna patterns used in the modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 

3.7a Vertical polarized omnidirectional . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 

3.7b Slant polarized omnidirectional . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 

3.7c Panel antenna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 

3.8 Candidate sites under investigation for simulation and feld evaluation. . . . . . . . . . . . . . . . . . . . . . . . 16 

3.9 Simulated aggregate emission results at candidate sensor locations inside DPA Camp Pendleton. . . . . . . . . . . 17 

3.10 Example of measured emissions over time by different sensors inside the always-on DPA Camp Pendleton. . . . . 18 

3.11 Example of statistics of mean power per day over year for all channels at a single sensor. . . . . . . . . . . . . . . 18 

3.12 Example of a workfow used to assess aggregate emissions in the CBRS band. . . . . . . . . . . . . . . . . . . . 18 

4.1 Spiral Development Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 

4.2 Sensor RF System Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 

4.3 Filter Measured Insertion Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 

4.4 LNA Gain and Reverse Isolation from Manufacturer Datasheet [1] . . . . . . . . . . . . . . . . . . . . . . . . . 23 

4.5 LNA Noise Figure from Manufacturer Datasheet [1] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 

4.6 RSA Power Spectrum with 3605 MHz CW Input Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 

4.7 RSA Fundamental Signal and Spur Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 

4.8 RSA Displayed Average Noise Level and Noise Figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 

4.9 RSA Gain Compression Measurement Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 

4.10 RSA 1 dB Gain Compression Point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 

4.11 Full system cascade block diagram illustrating the components and interconnects that are considered in estimating 

the cascaded gain and noise fgure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 

4.12 Cascaded gain (top) and noise fgure (bottom) of the full system showing the contribution of each component to 

the overall system gain of 32.1 dB and noise fgure of 4.9 dB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 

4.13 Cascaded signal level and compression characteristics of the full system when driven at the P1dB compression 

point of -18.32 dBm. This demonstrates how the signal compares to the compression point at each point in the 

system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 

4.14 Cascaded gain and noise fgure prediction of the system over the entire CBRS band. . . . . . . . . . . . . . . . . 29 

4.15 Gain drift shown of measurement sensor leveraged in NTIA’s TR-20-548 Technical Report. . . . . . . . . . . . . 30 

4.16 SEA Sensor Block Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 

4.17 Preselector Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 

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505

510

515

520

525

530

535

540

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550

DRAFT 

4.18 Preselector Assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 

4.19 Preselector Connector Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 

4.20 Thermal Analysis Results Without Solar Radiation and With Solar Radiation . . . . . . . . . . . . . . . . . . . . 34 

4.21 Preselector Outdoor Thermal Test Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 

4.22 SPU Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 

4.23 SPU Assembly (9U) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 

4.24 SPU Power Control Tray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 

4.25 SPU Power Control Tray Front Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 

4.26 SPU Power Control Tray Rear Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 

4.27 SPU RF Tray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 

4.28 SPU RF Tray Front Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 

4.29 SPU RF Tray Rear Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 

4.30 Power Distribution Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 

4.31 Full System Frequency Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 

4.32 Full System Noise Figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 

4.33 Full System 1 dB Compression Point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 

4.34 Channel power measurements with a continuous-wave (CW) stimulus well below the overload point. . . . . . . . 45 

4.35 Channel power measurements with a CW stimulus above the overload level. . . . . . . . . . . . . . . . . . . . . 45 

4.36 Channel power measurements with a noise-like stimulus greatly above the overload level. . . . . . . . . . . . . . 46 

5.1 High Level Software Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 

5.2 IQ Action Confg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 

5.3 Sensor Software Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 

5.4 Manager Software Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 

5.5 SEA Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 

5.6 Acquisition database table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 

6.1 Edge compute full band processing overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 

6.2 Edge compute channel processing overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 

6.3 infnite impulse-response (IIR) flter amplitude and phase response . . . . . . . . . . . . . . . . . . . . . . . . . 59 

6.4 IIR flter amplitude and phase response at the passband edge . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 

6.5 IIR flter amplitude and phase response at the stopband edge . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 

6.6 IIR flter z-plane analysis showing poles and zeroes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 

6.7 IIR flter step response analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 

6.8 Mean and max.-detected time series power statistics of simulated system noise . . . . . . . . . . . . . . . . . . . 62 

6.9 Mean and max.-detected time series power statistics of simulated system noise and “Radar 1” signal . . . . . . . . 63 

6.10 Mean and max.-detected time series power statistics of real-world signals recorded by the prototype sensor in 

Colorado . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 

6.11 Mean and max.-detected power spectral density result for a single 10 MHz channel of simulated system noise . . . 65 

6.12 Mean and max.-detected power spectral density result for a single 10 MHz channel of simulated system noise and 

“Radar 1.” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 

6.13 Mean and max.-detected power spectral density of real-world signals in the CBRS band, measured by the prototype 

sensor in Colorado . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 

6.14 Peak and root mean square (RMS)-detected PFP results for a single 10 MHz channel of simulated system noise . . 68 

6.15 Peak- and RMS-detected PFP results for a single 10 MHz channel of simulated system noise and “Radar 1.” . . . . 68 

6.16 Peak and RMS-detected PFP results for real-world signals in the 3590 MHz – 3600 MHz channel, measured by 

the prototype sensor in Colorado . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 

6.17 Peak and RMS-detected PFP results for real-world signals in the 3590 MHz – 3600 MHz channel, measured by 

the prototype sensor at NIT in Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 

6.18 Downsampled amplitude probability distribution (APD) result for a single 10 MHz channel of simulated system 

noise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 

6.19 Downsampled APD result for a single 10 MHz channel of simulated system noise and “Radar 1.” . . . . . . . . . 72 

6.20 Downsampled APD result for real-world signals in the 3590-3600 MHz channel, measured by the prototype sensor 

in Colorado . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 

6.21 Global metadata felds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 

7.1 Calibration block diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 

7.2 Comparison of Antenna Mismatches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 

7.3 Temperature Dependence of Gain, Hampton University Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 

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7.4 Residual Gain After Detrending of Channel 8, Hampton University Sensor . . . . . . . . . . . . . . . . . . . . . 83 

555 7.5 Residual Gain after Detrending Temperature Dependence of All Channels, Hampton University Sensor . . . . . . 84 

7.6 Temperature Dependence of Noise Figure channel 8, Hampton University Sensor . . . . . . . . . . . . . . . . . 85 

7.7 Residual Noise Figure after Detrending Temperature Dependence channel 8, Hampton University Sensor . . . . . 86 

7.8 Residual Noise Figure after Detrending Temperature Dependence All Channels, Hampton University Sensor . . . 87 

7.9 Gain Calibration in the Laboratory and as Deployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 

560 7.10 Noise Figure Calibration in the Laboratory and as Deployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 

7.11 Verifcation of Realized Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 

8.1 Surveyed sites at Marine Corps Base Camp Pendleton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 

8.2 Surveyed sites near East 1 E-DPA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 

8.3 Down-selected sites in the ground-based DPA (GB-DPA) Camp Pendleton from the initial survey of 22 sites. . . . 95 

565 8.4 First deployment of the prototype Rev 2 and 3 sensors at GMM in Boulder, CO. . . . . . . . . . . . . . . . . . . 97 

8.5 Pictures of the prototype sensors deployed in Virginia near the East 1 DPA. . . . . . . . . . . . . . . . . . . . . . 98 

8.5a Sensor staged for deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 

8.5b Preselector deployed at Norfolk International Terminal (NIT) . . . . . . . . . . . . . . . . . . . . . . . . 98 

8.5c Deployed sensor at Hampton University (HU) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 

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570 List of Tables 

4.1 Measured Filter Response Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 

4.2 Preselector Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 

4.3 SPU Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 

4.4 Sensor RF Specifcation Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 

575 6.1 Edge compute algorithm parameters overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 

6.2 IIR flter coeffcients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 

6.3 Coeffcients for the fat top window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 

8.1 Deployment parameters for the three prototype sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 

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Chapter 1 

580 Introduction 

Defense Information Systems Agency (DISA) Defense Spectrum Organization (DSO) proposed a measurement campaign to the National Advanced Spectrum and Communications Test Network (NASCTN) to measure emissions in 

the Citizens Broadband Radio Service (CBRS) band (i.e., 3550-3700 MHz) to allow Department of Defense (DoD) 

to collect data required for DISA DSO to ascertain the effectiveness of the sharing ecosystem between CBRS systems 

585 as managed by Spectrum Access Systems (SASs), and DoD systems as monitored by Environmental Sensing Capabilitys (ESCs). The collected data provides DISA DSO insight into the sharing ecosystem’s effectiveness, and track 

changes in the spectrum environment over time. 

This test plan is focused on the technical approach to collect emissions in the CBRS band in Always-On Dynamic 

Protection Areas (DPAs) through a series of deployed sensors and software to collect and report emissions for visual590 ization and analysis. The test plan describes the methodology, sensor and software design, data collection, modeling, 

and data visualisation/analysis to provide yearly reports and accompanying data on the collected CBRS emissions. 

1.1 Background 

1.1.1 NASCTN Process 

NASCTN projects follow a 5-Stage open, transparent, comprehensive process for developing and executing indepen595 dent, scientifcally based test plans, validating test results, and reporting fndings as shown in Figure 1.1. This serves 

as a common architecture across all NASCTN projects. 

Figure 1.1: NASCTN’s 5 Stage Process 

1 
1.1.2 CBRS Ecosystem 

The Federal Communications Commission (FCC) established the CBRS in the 3550-3700 MHz band, and created a 

frst of its kind three-tiered access and authorization framework to accommodate Federal incumbent and non-federal 

600 users which are tiered as Priority Access License (PAL) and General Authorized Access (GAA) users as shown in 

Figure 1.2. The FCC auctioned the band (Auction #105) for commercial use in July 2020. A system has been 

DRAFT 

established to enable this sharing, which is composed of two main parts: SASs which perform automated spectrum 

use coordination, and ESCs that detect incumbent use of the band and transmit that information back to the SASs. 

Figure 1.2: FCC’s three-tiered access for CBRS. Reference: https://ongoalliance.org/the-technology-behind-spectrumsharing-the-spectrum-access-system/ 

1.1.3 Test Request 

DoD 3.5 GHz Transition Plans focus on ecosystem validations, environmental assessments, and continued engagement 

on refning the CBRS infrastructure. One component was identifed for submission to NASCTN: to evaluate the 

effectiveness of the CBRS sharing ecosystem that manages the co-existence between Federal incumbent systems and 

commercial users, via an independent trusted agent. 

The DISA DSO submitted a CBRS Sharing Ecosystem Assessment (SEA) test request to NASCTN to achieve four 

objectives regarding the CBRS band: 

• Provide data driven insights into: 

1. Effcacy of permanent sharing between CBRS systems as managed by SASs and ESCs 

2. Noise foor measurements through continuous automated observations 

3. CBRS emissions within the Always-On DPAs at Army and Marine sites 

• Support 

1. The development and management of a measurement data repository 

This DISA DSO test request was divided into four major components: 

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1. Passive Observation: Characterize aggregate emissions within the CBRS band 3550-3700 MHz with and without 

DPA activations, in at least two coastal DPAs. Assess ecosystem performance to timely respond, and measure 

620 increase in background emissions due to wireless system deployments over time. 

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2. Passive Observation: Characterize aggregate emissions within the CBRS band 3550-3700 MHz in at least one 

Always-On DPA. Assess ecosystem performance to limit CBRS emissions in the Always-On DPA. 

3. Active Experimentation: Evaluate ESC detection performance in the Field (with Navy Ship). For a representative 

set of ESCs in the feld, assess true-positive and false-positive rates and independently verify incident power levels 

at ESC sensor locations. 

4. Long Term support - Collection and analysis of Passive Observations over 4 years, and support transfer of data to 

a DoD data repository. 

The project described in this test plan addresses item 2 and part of item 4 above for the Always-On DPA and is intended 

to be conducted over 5 years and is comprised of two phases: 

• Phase 1: Establish the measurement system requirements; develop, build, deploy a metrology sensor system to 

measure the CBRS ecosystem performance at DoD sites (e.g., Camp Pendleton, CA) and potentially other nonDoD sites. 

• Phase 2: Long-term analysis phase, with continuous emission level measurements and annual deep dive measurements and analysis. 

1.2 Test Methodology Overview 

This overview introduces 

• the Goals, of the measurement campaign within the Always-On DPA of Camp Pendleton, 

• the Scope of the work, 

• Phase 1 (deployable metrology sensor system) approach, 

• Next steps towards deployment, and 

• Phase 2 (long-term analysis) anticipated deliverables 

of a multi-year measurement of radio frequency (RF) emissions in the CBRS frequency band (3550 MHz to 3700 MHz) 

from within the Always-on DPA of United States Marine Corps (USMC) Camp Pendleton, California. 

This test plan presents the general methodology intended to collect and analyze the data. It provides the overall 

structure and objectives of the test, and a framework within to generate detailed plans, procedures, schedule, and 

resources. The technical approach and deliverables are anticipated to be refned over time so as to evolve with the 

changes in the ecosystem in conjunction with needs and inputs of stakeholders. Specifc details supporting this test 

plan (e.g., fnal sensor locations, quantities, design, test set-up, data collection and analysis) are still being determined 

and subject to change based on community input, site specifc regulations, and collected data over time, and will be 

captured in publicly released yearly reports. 

1.2.1 Objectives 

The goals of this effort include two major objectives by applying the aforementioned phases: 

1. Collect, validate, and publish data to provide insights into the ecosystems behavior at the Always-on DPA of Camp 

Pendleton. Validated data availability is to be nominally coincident with annual publication of NASCTN technical 

reports. 

2. Study and report on the background emissions within the CBRS-band as wireless systems continue to deploy over 

the timeframe of the project. 

1.2.2 Scope 

The test method discussed in this plan focuses on feld-captured emissions in the CBRS band and complimented by 

modeling and simulation. This measurement campaign is designed to collect data to assist Federal stakeholders to 

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independently confrm effective spectrum sharing (such as a potential evaluation of R2-IPM-05, “SAS’s shall manage 

CBSD aggregate interference levels for inland DPAs”). The project is not designed to ascertain individual Citizens 

Broadband Radio Service device (CBSD), SAS Administrators, or ESC operators compliance with established FCC 

rules for the CBRS band. 

1.2.3 Phase 1 Approach 

Data driven insights will be derived from the deployment of NASCTN developed leave behind sensor systems (chapter 4) which are autonomous and calibrated, traceable to National Institute of Standards and Technology (NIST)’s 

reference standard. The Camp Pendleton DPA is anticipated to be served with a minimum of 2 but up to 5 NASCTN 

SEA sensors, where sensor site selection undergoes iterative refnement. Candidate sites are proposed via surveys of 

the locations and corroborated using modeling-informed site selection (chapter 3). Ultimately, sites are chosen after a 

physical inspection for their suitability (section 8.2) that includes an evaluation of RF signaling, and sensor hosting requirements. To date, NASCTN has identifed two sites (subsection 8.2.5) that are undergoing fnalization with further 

sites under consideration and pending feasibility determination. 

In support of the metrology sensor platform development and to aid in the assessment of deployment methodology, this 

document showcases a fully functional, end-to-end, SEA sensor prototype-implementation and in-situ demonstration. 

Furthermore, the document shows the modeling and simulation provenance, and application in modeling informed site 

selection. 

The document is outlined as below: 

Modeling and Simulation (chapter 2) establishes the background for the adoption of reference model implementation, as applied to obtained obfuscated data products on CBRS deployments. 

Modeling informed site selection (chapter 3) covers the methodology by which modeling and simulation is applied 

in conjunction with SEA sensor siting criteria to provide for initial insights into the selection process from proposed 

sites to probable locations. 

Sensor System (chapter 4) discusses the spiral development process that led to a modular hardware design, and outlines the RF characteristics of the system, hardware implementation and key full-system characterization experiments 

and their results. 

Sensor Management (chapter 5) presents software control that leverages the spectrum-characterization and occupancy sensing (SCOS) framework for sensor management (SCOS manager) and on-board sensor actions (SCOS sensor). The deployed software suite discussion also covers failure recovery, and system monitoring and alerting. Additionally, this chapter presents the network topology and data back-haul. NASCTN team also outlines initial stages of 

a data repository. 

Sensor Data (chapter 6) gives detailed insights into the SEA projects candidate edge data payloads (power summary 

statistics and meta data) that are collected in-situ and tasked for ingestion into a data repository. The chapter frst introduces the data reference architecture, real-time waveform streaming and applied corrections and subsequently presents 

on the core data sets of Time-series, power spectral density, a novel concept of periodic frame power, and amplitude 

probability distribution. We note the anticipated data generation size and structure as well as make inferences on data 

aggregation. 

Calibration, Verifcation, and Acceptance tests (chapter 7) outlines the methods by which NASCTN anticipates 

to validate, characterize, and calibrate each of the SEA sensor nodes. The methodologies established in this chapter 

are fundamental to ascribing NIST-traceable measurements in the feld and ’clear-to-proceed’ for install of the sensor 

nodes. Confdence in the sensor measurements is of utmost import to the measurement campaign. 

Metrology Sensor Demonstration (chapter 8) highlights the demonstrated deployment of prototype sensors in relevant environments and the steps and methods that occurred towards the deployment. 

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1.2.4 Next steps towards Camp Pendleton 

The NASCTN team has visited 22 sites at the always-on DPA of Camp Pendleton and collected initial site details (see 

subsection 8.2.5 for full list). From the formative set of potential sites, modeling informed site selection methodology 

was applied and augmented with digital line-of-site view assessments to ascertain two candidate sites. The team is in 

preparation to fnalize these sites through: 

1. full flling the necessary programmatic requirements to obtain site access, and 

2. re-visiting the sites to fnalize details to establish details on SEA sensor installation parameters and siting. 

The fnalization of sites, and the information gleaned from the visits feeds into the SEA sensor hardware packaging. 

and environmental susceptibility details. These crucial details along with lesson’s learned from the prototype sensor 

system demonstration will evolve the hardware and software system architecture towards the ’feld-ready’ SEA sensor 

system. NASCTN notes details in evolution form prototype architecture in the Test plan’s accompanying community 

brief [2]. 

The annual NASCTN test report will contain the fnalized sensor deployment details. We anticipate the SEA sensor 

installation parameters to be critical to stakeholder analysis of NASCTN data sets. 

1.2.5 Phase 2 Anticipated Outcomes 

Throughout the project, NASCTN aims to establish, showcase, and iterate on novel methodologies for the aggregation of longitudinal sensor data and reference implementation models to provide insights in to the sharing ecosystem 

behavior within 3550 MHz to 3700 MHz. Community outreach efforts, such as this test plan document and adjudication, are utilized to gain insights and refne detailed test plans before they are fnalized. NASCTN will publish on 

the methodology and developed techniques, it’s applications and limitations, as well as make available pertinent publicly releasable data sets that encompass the data collection effort and augmentative modeling and simulation. These 

data sets will be made available for use by the CBRS stakeholder community to conduct further analysis and make 

decisions and recommendations on the ecosystems ability to adhere to the Part 96 rules governing Always-On DPAs 

To date, NASCTN anticipates the development of a methodology that leverages aggregation of longitudinal data from 

geographically dispersed, deployed sensors from within the Camp Pendleton DPA. In parallel, NASCTN anticipates 

adopting and refning upon available reference implementation models to further provide insights into the trends 

observable from provided obfuscated full activity dump data. 

1.2.6 Deliverables 

The following lists the deliverables of this effort: 

• A test report which includes a repeatable test methodology to collect CBRS ecosystem data to inform on the emission levels within the CBRS band for Always-On DPAs. 

• A set of validated data with uncertainties from feld collections and modeling to augment the interpretation of feld 

collects. 

• An initial set of analysis to interpret the collected data 

• A yearly report summarizing the data collected over the preceding year 

• Support establishing a repository for stakeholder access to collected, processed, and analyzed data 

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Chapter 2 

Modeling and Simulation 

Modeling and simulation are utilized to provide insight into the Citizens Broadband Radio Service (CBRS) ecosystem 

and to augment operational test design and evaluation of the National Advanced Spectrum and Communications Test 

Network (NASCTN) CBRS Sharing Ecosystem Assessment (SEA) project. 

2.1 Models 

This section describes the CBRS deployment model, propagation model, and Dynamic Protection Area (DPA) move 

list algorithm used for incumbent protection. While much of this text discusses using obfuscated full activity dump 

(FAD) data in Environmental Sensing Capability (ESC)-monitored DPAs (E-DPAs), the knowledge gained from this 

modeling was applied in identifying and evaluating potential sensor locations for the Always-On DPA for this test 

plan. 

2.1.1 Obfuscated Data on CBRS Deployment 

The Spectrum Access System (SAS) Administrators provided the National Telecommunications and Information 

Administration (NTIA) Institute for Telecommunication Sciences (ITS) obfuscated data on CBRS deployment and 

NASCTN received obfuscated FAD data for three selected areas. These include two areas near DPA West-14 and 

DPA East-1, and one area encompassing Boulder County and Broomfeld County in Colorado. For areas near DPA 

West-14 and DPA East-1, the obfuscated data were fltered out using the respective Category B Citizens Broadband 

Radio Service device (CBSD) neighborhood distance plus a 100 km distance buffer as shown in Figure 2.1a and Figure 2.1b. Detailed information related to DPAs, e.g., DPA-specifc neighborhood distances and protection criteria, can 

be found in DPA keyhole markup language (KML) fles provided by NTIA [3]. Since there is no DPA near Boulder, 

the geographical boundaries of Boulder County and Broomfeld County were used to flter out the obfuscated data as 

shown in Figure 2.1c. 

The procedure used to generate obfuscated FAD data can be summarized as follows: 

1. For each CBSD "registration" in the FAD data, "radioTechnology" within "airInterface" was obfuscated to replace 

any proprietary protocols. If "airInterface" was not "E_UTRA", "NR", or "CAMBIUM", it was replaced with 

"OTHERS". In addition, "latitude" and "longitude" were removed from the "installationParam", and a geographic 

identifer (GEOID) of the census block containing the latitude and longitude was added. 

2. The FAD data was then aggregated into distributions for each census block. Under each census block GEOID, there 

are three separate distributions for "Category_A", "Category_B", and "Unknown_Category" CBSDs. Further, each 

category contains distributions of: 

• "airInterface": contains the number of CBSDs that have "E_UTRA", "NR", "CAMBIUM", or "OTHERS" air 

interfaces. 

• "heightAGL", "heightAMSL", and "unknownHeight": each height distribution contains the number of CBSDs 

that have above ground level (AGL), above mean sea level (AMSL), or unknown heights such that hi ≤

hCBSD < hi+1, where hCBSD is the CBSD height, hi and hi+1 are two consecutive bin heights with 3 m 

bin size. 

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(a) DPA West-14 (b) DPA East-1 (c) Boulder and Broomfeld Counties 

Figure 2.1: Geographical masks used to flter out obfuscated data for three areas of interest. 

• "maxEirp": contains the number of CBSD grants that have max effective isotropic radiated power (EIRP) values 

such that maxEirpi < maxEirpCBSD ≤ maxEirpi+1, where maxEirpCBSD is the maxEirp value of the grant, 

maxEirpi and maxEirpi+1 are two consecutive bin values. The bins are every 5 dB down from 37 dBm except 

the lowest bin which includes values of 0, 1, 2 dBm. 

• "channel_use": contains the number of CBSD grants operating on each 10 MHz channel from 3550 MHz to 

3700 MHz. 

• "bandwidth": contains the number of CBSD grants that received each specifed grant bandwidth. 

• "count": contains a count of the total number of CBSDs. 

• "indoor_deployment_count": contains a count of the total number of indoor CBSDs. 

The obfuscated data were used to generate randomized CBSD deployments for each area. Two categories of CBSDs, 

i.e., low-power indoor Category A CBSDs and high-power outdoor Category B CBSDs, were generated separately for 

each census block GEOID provided in the obfuscated data fle. Note that "Unknown_Category" CBSDs have not been 

populated in the latest obfuscated data. And for each category, the CBSD registration request data and grant request 

data were randomly generated from the obfuscated distributions, and then the grants were randomly assigned to the 

CBSDs. Note that in the CBRS standards [4], a "grant" is an authorization provided by a SAS to a CBSD, subject to a 

Heartbeat exchange, to transmit using specifed operating parameters; and a CBSD may have multiple grants. 

2.1.2 Propagation Model 

The Wireless Innovation Forum (WInnForum) CBRS standards for SAS general requirements specify in [5, R2-SGN03] that a SAS shall use the NTIA ITS Irregular Terrain Model (ITM) [6] (also known as the Longley-Rice model) in 

point-to-point mode for use in DPA protection for Initial Certifcation. The ITM model is a terrain-based model and 

it does not include clutter loss. However, to account for building attenuation, which is also absent in the ITM model, 

additional loss of 15 dB is ascribed if the CBSD is located indoors [5, R2-SGN-03]. Other parameters used in the ITM 

model are provided in another SAS general requirement [5, R2-SGN-17]. 

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800 An open-source reference implementation of the ITM model is available at the WInnForum SAS Testing and Interoperability repository [7]. Terrain and other data used by the model are available at the Common WInnForum Data Libs 

[8]. 

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2.1.3 WInnForum Reference DPA Move List Algorithm 

Regulatory rules [9] require that the SAS administrators manage their CBSD transmissions to protect the operations 

of existing incumbents in the band. To meet the requirement, the WInnForum CBRS standards specify the DPA 

protection procedure, also known as the move list algorithm, to be executed by all SASs. Given a set of CBSD 

transmissions having or requesting grants that overlap in frequency with a protected frequency range, the move list 

algorithm identifes which transmissions must be suspended (and possibly relocated to a different channel) to avoid 

excessive interference in a protected federal incumbent area. 

Details of the algorithm and its reference implementation can be found in [5, R2-SGN-24] and [7]. In general, for 

any protected frequency range ch and for any protection point p within a given DPA, the move list algorithm frst 

determines a set of Nc CBSDs having or requesting a grant that includes any portion of the protected frequency range 

ch, and that are within a neighborhood of the protection point p. It then computes the path loss from each grant to the 

protection point p using the ITM model described in Section 2.1.2. The algorithm applies a reference receive antenna 

pattern for protection points with a main lobe beamwidth of 3 degrees, a reference gain of 0 dBi within the beamwidth, 

and a reference gain of −25 dBi outside of main lobe [5, R2-IPM-04]. The algorithm then chooses a subset of the 

grants that must be suspended (relocated) such that the 95th percentile of the aggregate emission, from Nc grants to 

the protection point p with a minimum of 2000 Monte Carlo trials [5, R2-IPM-03], is below a predefned threshold. 

The grants that must be removed to meet the protection threshold are placed on the move list. This process is repeated 

for every protection point in the protection area DPA. The move list for the entire protection area DPA is the union of 

the move lists of the protection points inside the DPA. 

The primary output of the move list algorithm is the move list. For the purpose of this modeling and simulation, a 

neighbor list and a keep list for the protection area DPA are also considered. The neighbor list is defned as the union 

of the neighbor lists of the protection points inside the DPA. Whereas, the keep list is defned as a complement of the 

move list, and it contains grants that may remain active on the protected channel when the DPA is active. 

2.2 Simulation 

This section showcases examples of DPA move list simulation results applicable to Task 2. From the obfuscated data, 

randomized CBSD grant deployments can be generated for each 10 MHz channel in the 3550-3650 MHz band and 

input to the reference implementation of the DPA move list algorithm. The outputs include the neighbor list, move 

list, and keep list grants for each 10 MHz protected channel for a DPA under consideration. Examples of simulation 

results for Task 1 and Task 2 are shown in the following subsections. 

The objective is to inform on the emissions within the CBRS band over time from within the always-on DPA. Note 

that this objective is inherently decoupled from the presence of CBSDs. 

Challenges to ascertain an alternate always DPA from Camp Pendleton: 

• There has been no other always-on DPA offered as a point of interest for the Department of Defense (DoD). 

• NASCTN has limited data sets to ascertain CBRS likelihood to deploy near a particular DPA. 

2.2.1 Examples of Task 2 Simulation Results 

For Task 2, the always-on DPA at Camp Pendleton is considered. The DPA is used to protect incumbent systems 

operating below 3500 MHz. The protection criteria, e.g., protection thresholds and neighborhood distances, vary 

by 10 MHz channels in the 3550-3650 MHz band[3]. In this simulation, a default method, implemented by the 

WInnForum [6], is used to generate approximately 200 protection points along the boundary and inside the DPA for 

move list calculation. Since the DPA is always-on, the grants on the move list are automatically suspended until they 

come out of the move list in the next move list calculation. 

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Figure 2.2 shows the examples of grants on the neighbor list, move list, and keep list near the always-on DPA Camp 

845 Pendleton. There are not many CBSD grants in the neighborhood of the DPA Camp Pendleton. Note that the CBSD 

grant locations shown in the fgures are only for illustration purposes due to proprietary concerns. 

(a) Example of neighbor list grants (b) Example of move list grants (c) Example of keep list grants 

Figure 2.2: Examples of simulation results for always-on DPA Camp Pendleton. 

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Chapter 3 

Modeling Informed Site Selection 

To assess the effectiveness of spectrum sharing in the Citizens Broadband Radio Service (CBRS) ecosystem, the 

850 National Advanced Spectrum and Communications Test Network (NASCTN) Sharing Ecosystem Assessment (SEA) 

project will need to deploy multiple sensors in the vicinity of Dynamic Protection Areas (DPAs). Given the vast 

geographical areas of DPAs, modeling is used as a tool to help informing site selections for SEA sensors to measure 

emissions in the CBRS band. 

3.1 Methodology Overview 

855 This section describes the aggregate emissions model used to calculate Citizens Broadband Radio Service device 

(CBSD) emissions at a given SEA sensor location. An example of the calculation is detailed subsequently in Section 3.1.2 for a prototype sensor deployed at Green Mountain Mesa (GMM) in Boulder, Colorado. 

3.1.1 Applied Aggregate Emission Model 

Given a SEA sensor location s and any 10 MHz channel ch in the 3550-3700 MHz band, the aggregate emission 

860 model frst determines a set of N grants that are within the neighborhood or vicinity of the sensor s and requesting 

or operating on any portion of the channel ch. The neighborhood around a sensor is defned as the area in which 

transmission of a CBSD grant can be detected by the sensor. In the current simulation, the sensor’s neighborhood is 

considered as the same as the monitoring DPA’s neighborhood. The model then computes the emission contribution 

Ii (dBm) from each individual grant i, for 1 ≤ i ≤ N, to the sensor s. Finally, it sums in linear power emission 

865 contributions from all N grants together to obtain the aggregate emission received at the sensor. 

When computing the emission contribution Ii (dBm) of an individual grant i, the model uses a simple link budget 

equation as follows: 

Ii (dBm) = EIRPi − Li + Gs,i (3.1) 

where EIRPi is the effective isotropic radiated power (EIRP) value of the grant i within the channel ch (dBm), Li is 

the path loss from the grant i to sensor s (dB), and Gs,i is the 2D antenna gain of the sensor s in the direction of the 

grant i (dBi). 

Let’s defne {I1 (dBm), · · · , Ii (dBm), · · · , IN (dBm)} as a set of N independent, but not necessarily identical, random 

variables, each representing the emission contribution from a grant i to sensor s on channel ch. The associated emission 

= 10Ii (dBm)/10 contribution in linear scale (mW) can be computed as Ii . Consequently, the aggregate emission I (mW) 

of N grants is the sum of the emission contribution of grants I1, · · · , Ii, · · · , In as follows 

X X

N N

10Ii (dBm)/10 I = Ii = (3.2) 

i=1 i=1

and I (mW) can be converted to log scale by I (dBm) = 10 log10 I (dBm). 

10 
870 3.1.2 Example of Aggregate Emission Calculation 

To further elaborate on the aggregate emission calculation, an example of such calculation is presented for a small area 

in Boulder, Colorado. Since there are measurements collected at a prototype SEA sensor in the area (see Section 8.3.1 

for more details), it’s benefcial to compare the aggregate emission calculated using obfuscated full activity dump 

(FAD) data with the measured power per channel in the 3550-3700 MHz band. Furthermore, Monte Carlo simulations 

875 

880 DRAFT 

are performed on different CBSD deployments to determine if the random deployments from the same obfuscated data 

produce signifcantly different results. 

3.1.2.1 Randomized CBSD Deployment and Sensor Location 

A randomized CBSD deployment was generated from the obfuscated FAD data for Boulder County and Broomfeld 

County in Colorado. The obfuscated FAD data was provided by the Spectrum Access System (SAS) Administrators 

in January, 2023. More Category B CBSDs than Category A CBSDs have been deployed in the area. In total, there are 

more than 250 CBSDs having approximately 800 grants and operating on 15 channels in the 3550-3700 MHz band. 

Figure 3.1 shows an example of CBSD grant deployment in the area. The white markers represent the CBSDs, i.e., 

markers with dots are Category B and markers without dots are Category A. Note that the CBSD grant locations shown 

in the fgures are only for illustration purposes due to proprietary concerns. 

Figure 3.1: An example of a CBSD deployment in Boulder County and Broomfeld County, Colorado. 

The GMM sensor location is shown as a green pin in Figure 3.1. And Table 3.2 lists the parameters and values of the 

sensor. A 2D panel antenna is installed at 4.09 m height above ground level (AGL), with 12 degrees azimuth relative 

to True North and 0 degree down-tilt. 

3.1.2.2 Measured Power vs. Estimated Aggregated Emissions Comparison 

Since there is no DPA near the Boulder area, an aggregate emission from all CBSD grants in Boulder and Broomfeld 

Counties to the GMM sensor location is estimated for every 10 MHz channel in the 3550-3700 MHz band. An analogy 

is drawn between the estimated aggregate emission and the measured power by the prototype SEA sensor at the GMM 

location. 

Figure 3.3 shows an example of median root mean square (RMS) power, max peak power, median RMS periodic 

frame power (PFP), and max peak PFP in 10 MHz channel collected by a NASCTN SEA prototype revision 3 sensor 

885 

890 

895 at the GMM location on December 6, 2022. More information on sensor data products can be found in Chapter 6. As 

shown in the fgure, CBSD activities can be found in many channels (e.g., channels 1-4 and 11-14). Measurements in 

the last channel 15, centered at 3695 MHz, were in a sensor overload condition, which might affect the absolute power 

levels in that channel as well the 2 lower adjacent channels 13 and 14 which are centered at 3675 MHz and 3685 MHz, 

11 
DRAFT

Figure 3.2: Sensor placement information at GMM, Boulder, Colorado. 

respectively. 

Figure 3.3: An example of measured power collected by a NASCTN prototype SEA sensor at the GMM location on 

December 6, 2022. 

900 In addition, Figure 3.4 shows the median RMS power and max peak power collected by a NASCTN prototype SEA 

sensor at the GMM location from November 29, 2022 through December 7, 2022. The measurements were taken 

continuously with an on channel dwell time of 4 and channel revisit times of about 90 s, where each channel is 

measured once in a consecutive sweep. 

12 
DRAFT

Figure 3.4: Measured powers collected by a NASCTN prototype SEA sensor at the GMM location from November 

29, 2022 to December 7, 2022. 

On the other hand, Figure 3.5 illustrates an example of the estimated aggregate emission in 10 MHz channel from a 

905 randomized CBSD deployment to the GMM sensor location. We use different propagation models and confgurations 

to estimate the path loss. In addition to the Irregular Terrain Model (ITM) model with 95th percentile from 2000

Monte Carlo iterations, the ITM model with median or reliability of 0.5 and the Friis free space model are used to 

compute the path loss from the CBSD to the GMM sensor location. 

Figure 3.5: An example of estimated aggregate emission at the GMM sensor location. 

The ITM model, with 95th percentile (as shown in red markers in the fgure) and median (in blue markers), and the 

910 free space model (in green markers) provide similar aggregate emission results, except the last channel centered at 

13 
DRAFT 

3695 MHz. To better visualize the emission results, the markers are shifted slightly in the x-axis but are still within 

the measured 10 MHz channel. 

It is observed that the estimated aggregate emissions are much higher than the measured powers in all channels. It 

is found that the emissions from a few CBSDs, which are deployed within 3 km from the GMM sensor and in line915 of-sight paths, dominate the aggregate emission levels. There are numerous factors that contribute to the discrepancy 

between the measured powers vs. estimated aggregate emissions such as 

1. Inaccurate representation of the real-time CBSD deployment (e.g., location, antenna characteristics, transmit 

power, channel occupancy, etc.) generated from the quarterly obfuscated FAD data, and 

2. Clutter has not been included in the aforementioned propagation models. 

920 3. Interference contributions from CBSDs that might not be independent. 

The aim is to determine these factors and resolve them so that the modeling results can be as close as possible to 

the real measurements. Getting more accurate representation of the real-time CBSD deployment could be feasible; 

however, adjudication and recommendations on propagation models and their feature set such as incorporating clutter 

is beyond the scope of the NASCTN CBRS SEA project. 

925 3.1.2.3 Monte Carlo Simulation of CBSD Deployments 

Monte Carlo simulation is used to determine if random CBSD deployments derived from the same obfuscated FAD 

data produce signifcantly different results. The obfuscated FAD data are obfuscated and aggregated from the FAD 

fles by the SAS Administrators every quarter. The obfuscated FAD data have proven to be rich and informative, yet 

the data do not refect real-time activities of CBSD deployment. 

930 Figure 3.6 shows the estimated aggregate emissions for 100 different CBSD deployments. Since the census blocks 

near the GMM sensor are small in area, the CBSD locations do not vary substantially in effective path loss. It is also 

observed that the random deployments from the same obfuscated data produce similar aggregate emission results for 

both ITM and Friis free space models. This is likely due to the ITM model being a step wise model and heavily relies 

on the free space model for line-of-sight transmissions. 

Figure 3.6: Examples of estimated aggregate emissions from 100 randomized CBSD deployments at the prototype 

GMM sensor in Boulder, Colorado. 

935 The obfuscated FAD data could be suffcient for initial estimate of aggregate emissions at a given SEA sensor location. 

14 
DRAFT 

However, non-obfuscated FAD data with more detailed information can certainly improve the accuracy of aggregate 

emission calculation, which better informs site selections for SEA sensors. 

3.2 SEA Sensor Siting 

3.2.1 Site Criteria 

940 The NASCTN SEA sensor locations are selected based on initial site surveys to ensure the availability of shelter and 

power as well as easy access for maintenance of the sensors over extended periods of time. High elevated sites are also 

preferable to better detect CBSD emissions at reduced obstructions and potential for clutter. Modeling is then utilized 

as a tool to further assist in selecting the sensor locations. 

The noise level per 10 MHz channel at the sensor is computed as follows 

Noise = −174 (dBm/Hz) + 10 log10 BW + NF (3.3) 

The channel bandwidth BW is set to 107 Hz and the noise fgure NF used for this modeling exercise is a projected 

945 5 dB, which results in the noise foor to be equal to −99 dBm/10 MHz. Note that the noise fgure used for modeling 

may differ slightly from the values of the deployed prototype systems as described in Section 4.4. 

Three different 2D antenna patterns are used in the modeling as shown in Figure 3.7. They include a vertical polarized 

omnidirectional antenna, a slant polarized omnidirectional antenna, and a panel antenna. The omnidirectional antennas 

provide omnidirectional coverage in azimuth dimension and directive coverage in elevation dimension. Whereas, the 

950 panel antenna provides coverage from a planar array above the ground plane. 

(a) Vertical polarized omnidirectional (b) Slant polarized omnidirectional (c) Panel antenna 

Figure 3.7: Information of 2D antenna patterns used in the modeling. 

15 
DRAFT 

3.2.2 Preliminary Aggregate Emission Results 

The objective of the modeling in Task 2 is to estimate the aggregate emissions at candidate sensor locations inside 

the always-on DPA Camp Pendleton. The DPA is used to protect ground based incumbent systems operating below 

3500 MHz. And the protection criteria, e.g., protection thresholds and neighborhood distances, vary by 10 MHz 

955 channels in the 3550-3650 MHz band [3]. Figure 3.8 depicts candidate sensor locations inside DPA Camp Pendleton. 

Most of these locations were recently surveyed by the NASCTN SEA team which will be described in detail in 

Section 8.2. The antenna height at each location is assumed to be 4 m AGL. 

Figure 3.8: Candidate sites under investigation for simulation and feld evaluation. 

Figure 3.9 shows examples of the aggregate emission results at candidate sensor locations for the 3630-3640 MHz 

channel. Since Camp Pendleton is an always-on DPA, only emissions from the keep list are estimated. Although three 

960 2D antenna patterns, described in Section 3.2.1, were used in the modeling, only the results for the vertical polarized 

omnidirectional antenna were presented as an example. The horizontal dashed line is the projected noise foor at 

−99 dBm/10 MHz. The emission levels are at or below the sensor internal noise foor at most candidate locations. In 

this example, for the 3630-3640 MHz channel, emissions are projected to be detectable above the noise foor only at 

candidate locations 8 and 27. 

965 3.2.3 Key Technical Challenges with Current Simulation and Modeling 

Several key technical challenges with the current simulation and modeling have been identifed that hinder the accuracy 

of the modeling results. The preliminary modeling results presented in previous sections show that the aggregate 

emissions could be overestimated, as compared to the real measurements, potentially due to the following reasons: 

• Maximum requested EIRP values of the CBSD grants have been used in the modeling, while in practice, approved 

970 CBSD grants may operate at lower maximum allowable EIRP values or may not transmit at all. 

16 
DRAFT

Figure 3.9: Simulated aggregate emission results at candidate sensor locations inside DPA Camp Pendleton. 

• The effects of activation of adjacent DPAs or other protection entities nearby, e.g., Environmental Sensing Capability 

(ESC) protection zones, have not been considered in the modeling. 

• Clutter has not been taken into account in the propagation models used to compute path loss from the CBSD grant 

to the sensor location. 

975 3.3 Informing Data Products 

This section sketches out some initial ideas to aid with the data products development. Chapter 6 describes the other 

data products in detail. Data products and data analysis are used to a) present the aggregate emissions in the CBRS 

band due to deployment of systems over time, and b) validate the always-on DPA protection area at Camp Pendleton. 

For the DPA Camp Pendleton, the protection threshold thresholdch for each 10 MHz channel ch can be obtained 

980 from the GB-DPAS.kml fle at [3]. Let’s defne Ps,t,ch (dBm/10 MHz) as the aggregate emission measured by sensor

s at time t in channel ch. Figure 3.10 illustrates an example of the measured emissions over time, as compared with 

the threshold thresholdch, by different sensors inside the DPA. The time resolution ∆ts at sensor s can be varied 

depending on the analysis types and capabilities of the sensor. 

An alternative approach is to show the histograms of mean measured emission per a period, e.g., hour or day, over a 

985 longer period of time, e.g., year, for all channels as shown in Figure3.11. 

To assess the measured aggregate emission Ps,t,ch against the protection threshold thresholdch on channel ch, a simple algorithm as shown in Figure 3.12 can be carried out. Note that, conversion of the protection threshold thresholdch

might be required, since the threshold obtained from the GB-DPAS.kml fle was derived by the National Telecommunications and Information Administration (NTIA) with different receive system assumptions. 

17 
DRAFT

Figure 3.10: Example of measured emissions over time by different sensors inside the always-on DPA Camp Pendleton. 

Figure 3.11: Example of statistics of mean power per day over year for all channels at a single sensor. 

Figure 3.12: Example of a workfow used to assess aggregate emissions in the CBRS band. 

18 
DRAFT 

990 Chapter 4 

Sensor System 

19 
4.1 Sensor Spiral Development Process 

A custom sensor has been developed to monitor and analyze radio frequency (RF) emissions in the Citizens Broadband 

Radio Service (CBRS) band. The data produced by a network of these sensors supports Sharing Ecosystem Assess995 ment (SEA) tasks 1 and 2. A spiral development process was utilized to rapidly provide benchtop prototypes, allow 

incremental development as system requirements solidifed, and support the evolution of the spectrum-characterization 

DRAFT

and occupancy sensing (SCOS) framework. The development process is comprised of 5 stages as shown in Figure 4.1, 

and the frst three prototype phases will inform the fnal two design iterations. 

Figure 4.1: Spiral Development Process 

4.2 Sensor Overview 

1000 The SEA sensor was designed to analyze RF emissions in the 3550 to 3700 MHz CBRS band. It is a complete

system that captures the RF spectrum, computes power statistics and their distributions across time and frequency, 

and transfers these data products to a centralized repository for post-analysis. The following sections provide further 

details on the sensor design, and refer specifcally to the fnal prototype revision of the system (revision 3) unless 

otherwise noted. 

1005 4.2.1 RF System 

Figure 4.2 is a block diagram of the complete sensor RF system. At the start of the RF signal path is a single pole 

double throw (SPDT) switch (designator SWT in block diagram) which selects either the antenna (ANT) or a calibrated 

noise source (NSR) as the input to the system. The antenna receives RF emissions in the CBRS band, while the noise 

source is used to perform periodic calibration as described in Section 4.2.3. Following the RF switch are an isolator 

1010 (ISL) and a bandpass flter (BPF). The flter rejects emissions outside of the CBRS band, while the isolator helps to 

present a consistent impedance to the input of the flter regardless of the state of the RF switch. This minimizes a 

variable between the calibration path and antenna path, and ensures a more accurate calibration. Following the flter 

is an RF limiter (LMT) to provide protection against strong in-band emissions. Next, a low noise amplifer (LNA) 

(AMP1) provides the gain needed to compensate for system losses and minimize system noise fgure. 

1015 Following the LNA are a coaxial cable (CBL) and attenuator (ATT). A coaxial cable up to 30.5 m long is accounted 

for in the system gain budget. This allows for physical separation between groups of components, which provides 

20 
1020

1025

1030

1035

1040

1045

DRAFT

Figure 4.2: Sensor RF System Diagram 

fexibility in how the sensor is deployed. The group of components prior to and including the LNA (AMP1) can 

be close to the antenna to minimize system noise fgure, while the remaining components which tend to be larger 

and consume more power can be located farther away. The attenuator following the coaxial cable is used to make 

adjustments to compensate for cables of varying lengths. The attenuator value is adjusted as needed to maintain a 

constant net insertion loss, resulting in consistent RF performance regardless of system variations due to installation. 

The fnal components in the RF signal path are the second amplifer stage (AMP2) and real-time spectrum analyzer 

(RSA). The second amplifer stage preceding the RSA provides additional gain needed maintain a low overall system 

noise fgure. While the RSA can operate as a fully-featured and stand-alone test instrument, in this application it serves 

as a tunable RF digitizer. The RF signal at the input is converted to a digital complex baseband signal with in-phase 

and quadrature (IQ) components. Subsequent processing on the IQ data is performed on the embedded computer 

(CMP). 

Tables 4.2 and 4.3 provide a complete list of components and part numbers used in the sensor. Additional details on 

select components are provided in the following sections. 

4.2.1.1 Antenna (ANT) 

The sensor uses an L-Comm HG3509U-PRO [10] antenna. This antenna is vertically polarized with 9 dBi gain and 

has a specifed operational bandwidth of 3400 to 3700 MHz. The radiation pattern is omnidirectional in azimuth and 

has an 11° 3 dB beamwidth in elevation. This pattern is advantageous because an omnidirectional pattern allows the 

sensor to observe in a 360° feld of regard in azimuth. Since CBRS systems are terrestrial systems, a more directive 

elevation pattern is useful as neighboring Citizens Broadband Radio Service devices (CBSDs) will be predominately 

located within the 3 dB beamwidth of the sensor antenna’s elevation pattern. 

The sensor has a single input for the RF path. For simplicity, a vertically polarized antenna is leveraged to observe RF 

emissions in the CBRS band. CBSDs typically leverage dual slant 45° polarization. The measurement system may 

incur up to a 3 dB polarization loss from the measured dual slant 45° and circularly polarized emitters in the CBRS 

ecosystem. An additional polarization loss will be incurred from horizontally polarized emitters broadcasting in the 

measurement band. 

4.2.1.2 Bandpass Filter (BPF) 

A custom-designed cavity flter from K&L Microwave, part number 13FV40-3625/U150-O/O, is used to protect the 

LNA from potentially strong out-of-band emissions while allowing the CBRS band to pass with minimal insertion 

loss. Since the flter is located prior to the LNA (AMP1), it’s insertion loss directly impacts the overall system noise 

fgure. The flter was specifed to have an insertion loss of less than 1.5 dB at the center frequency and no more than 

21 
DRAFT 

Table 4.1: Measured Filter Response Characteristics 

Insertion Loss (3625 MHz) -1.3 dB 

CBRS Band Flatness (3550-3700 MHz) 0.85 dB 

Filter Shape 

Parameter Low High Bandwidth 

1 dB Bandwidth 3545 MHz 3705 MHz 160 MHz 

3 dB Bandwidth 3535 MHz 3718 MHz 183 MHz 

>20 dB Rejection <3528 MHz >3726 MHz 198 MHz 

>40 dB Rejection <3519 MHz >3734 MHz 215 MHz 

>60 dB Rejection <3505 MHz >3747 MHz 242 MHz 

1 dB roll-off across the range of CBRS frequencies, while providing suppression of at least 57 dB for all frequencies 

either 50 MHz below or 50 MHz above the CBRS band. 

The realized flter was characterized using a Rohde & Schwarz ZNB8 vector network analyzer and the resulting 

1050 insertion loss, shown in Figure 4.3, indicates that it meets these specifcations. A summary of measured flter response 

characteristics is provided in Table 4.1. 

Figure 4.3: Filter Measured Insertion Loss 

4.2.1.3 Low Noise Amplifer (AMP1) 

The frst LNA in the receive chain of the measurement sensor is the LNA02004000A, 0.2 GHz to 4.0 GHz wideband 

low noise amplifer from LiConn, Inc. This LNA has a typical gain of 29 dB as shown in Figure 4.4, and a typical 

1055 noise fgure of 1.3 dB as shown in Figure 4.5 [1]. The high gain allows for the noise fgure to be maintained between 

the portion of the RF sensor co-located with the antenna and the input of the RSA. This allows additional fexibility 

for system deployment. The RSA may be located over 30.5 m away from the antenna without degradation to RF 

performance of the system. This LNA is rated to have a wide operational temperature of -45°C to +85°C which 

simplifes the enclosure design by not requiring active heating or cooling. 

22 
DRAFT

Figure 4.4: LNA Gain and Reverse Isolation from Manufacturer Datasheet [1] 

Figure 4.5: LNA Noise Figure from Manufacturer Datasheet [1] 

Realtime Spectrum Analyzer (RSA) 

A market survey was conducted to identify RSAs, spectrum monitoring receivers, and software-defned radios that are 

tunable over the CBRS band, can acquire at least 20 MHz instantaneous bandwidth and can transfer baseband IQ data 

over a universal serial bus (USB) or Ethernet interface. Thirteen candidates were identifed and evaluated based on 

RFs performance, size, power consumption, operating temperature range and cost. Of those thirteen units, three units 

1060 4.2.1.4 

1065 were selected to undergo laboratory testing to examine RF performance, and ultimately the Tektronix RSA507A was 

selected. 

The RSA507A is tunable over the frequency range 9 kHz to 7.5 GHz and has an instantaneous bandwidth of up to 

23 
DRAFT 

40 MHz. It has a USB 3.0 interface for device confguration and for transferring IQ data to the host computer when 

operating in IQ acquisition mode. It draws 15 W, operates over the temperature range -10°C to +55°C and comes in a 

1070 30.0 cm x 27.2 cm x 6.9 cm rugged package. 

Laboratory measurements were performed to verify critical datasheet specifcations [11] and to examine RF performance under the expected operating conditions for the SEA sensor application. Several of these measurements are 

presented here. 

1075 

1080 

1085 

First, the amplitude variation and spur levels over the RSA’s maximum instantaneous bandwidth of 40 MHz were 

measured. The instrument was confgured with a center frequency of 3600 MHz, reference level of -40 dBm, and IQ 

acquisition sample rate of 56 Msps (which, by design, is the sample rate corresponding to 40 MHz usable bandwidth). 

An RF signal generator was then used to sweep a -40 dBm continuous-wave (CW) signal across the band in 1 MHz 

steps, and IQ data was acquired at each 1 MHz step. A fast Fourier transform (FFT) analysis was performed on these 

IQ captures and the fundamental signal level and spur levels were measured. Figure 4.6 shows an example of the FFT 

analysis performed on the capture when the input frequency was 3605 MHz. The green marker indicates the frequency 

and power level of the input signal, and the red markers indicate the frequencies and power levels of the strongest three 

spurs. Note that with an IQ sample rate of 56 Msps, the baseband FFT has a span of -28 MHz to +28 MHz. However, 

only the middle 40 MHz, or a span of -20 MHz to +20 MHz, is intended to be usable bandwidth. The frequency axis in 

Figure 4.6 has been modifed to indicate the RF input frequency rather than the baseband frequency of the acquisition, 

and frequencies outside of the usable bandwidth have been shaded orange. 

Figure 4.6: RSA Power Spectrum with 3605 MHz CW Input Signal 

Figure 4.7 shows the resulting fundamental signal level and relative spur levels as the input signal is swept across 

the band. The plot of the fundamental signal level illustrates an amplitude variation of approximately 0.2 dB across 

the 40 MHz band. Note that conclusions about amplitude accuracy should not be drawn from this plot because the 

insertion loss of the short coaxial cable between the signal generator and RSA was only estimated. The plot of the 

1090 spur levels shows that for all input frequencies, the largest spur remains at least 70 dB below the fundamental. The

result is shown in Figure 4.8. 

Next, to characterize the noise performance of the RSA, its input was terminated with a 50Ω load and IQ captures 

were obtained. With the RSA still confgured for center frequency of 3600 MHz and sample rate of 56 Msps, and 

24 
DRAFT

Figure 4.7: RSA Fundamental Signal and Spur Levels 

with the internal preamplifer enabled, the RSA reference level was increased from -54 dBm to 0 dBm in 6 dB steps. 

1095 At all reference level settings, the RSA’s internal input attenuator was set to 0 dB to optimize noise performance. IQ 

data was acquired at each reference level setting, and from the IQ data the displayed average noise level (DANL) was 

computed. The result is shown in Figure 4.8. The minimum measured DANL of -155.6 dBm is within the range 

specifed in the datasheet [11]. 

Figure 4.8: RSA Displayed Average Noise Level and Noise Figure 

As noted on the right axis of Figure 4.8, the RSA’s noise fgure (NF) is equivalent to the difference between DANL 

1100 and thermal noise power (nominally -174 dBm/Hz for the expected operating temperature range). It is important to 

know the RSA’s noise fgure for its given reference level to determine the overall system noise fgure. 

25 
DRAFT 

The gain compression of the RSA was also measured. With the RSA still confgured for center frequency of 3600 MHz 

and sample rate of 56 Msps, and with the internal preamplifer enabled, an RF signal generator was used to produce 

a 3600 MHz CW signal at increasing power levels. At each power level, IQ data was acquired and FFT analysis 

1105 was performed to determine the signal level measured by the RSA. The 1 dB gain compression (P1dB) point was 

determined by observing the measured signal level over the range of known input levels. 

An example P1dB measurement made with an RSA reference level of -30 dBm is shown in Figure 4.9. In this plot the 

red points indicate input signal levels where the RSA reported an "ADC overrange" condition. For all points where 

analog-to-digital converter (ADC) overrange does not occur, the measured signal power closely matches the input 

1110 power and no gain compression is exhibited. When an ADC overrange condition does occur, large distortion products 

in the power spectrum draw power away from the fundamental, and as a result the measured fundamental signal level 

drops abruptly. Due to this characteristic, the P1dB point is defned to be the highest input power that does not cause 

an ADC overrange condition. While Figure 4.9 shows the gain compression measurement with an RSA reference 

level of -30 dBm, this characteristic is typical of most reference levels where this measurement was performed. 

1115 The process of measuring P1dB was repeated for a range of RSA reference level settings. The resulting P1dB over the 

range of RSA reference levels is shown in Figure 4.10. 

Figure 4.9: RSA Gain Compression Measurement Example 

4.2.1.5 Embedded Computer (CMP) 

Processing of the IQ data is performed on a SimplyNUC NUC11TNHI7Z small form factor computer. This computer 

has an 11th Gen Intel® CoreTM i7 processor and 32 GB RAM, and runs the Ubuntu operating system. The computer 

1120 ingests IQ data from the RSA via a USB 3.0 interface and performs processing as described in Section 6.1.2. 

4.2.2 Cascade RF Analysis 

System cascade analysis has been generated to optimize the RF chain to the signal characteristics expected at the 

antenna input of the sensor. The goals are to optimize the signal levels (min/max) in the signal processing chain while 

providing a low overall noise fgure by adding a minimal amount of noise to the signal. Once the RF signal is digitized 

1125 in the signal processor, the overall signal profle is frozen, so it is critical to optimize the signal before it is passed on 

to the digitizer in the signal analyzer. To design the system, components need to be selected to address overall system 

constraints and goals including: 

26 
1130

1135

1140

1145

1150

DRAFT

Figure 4.10: RSA 1 dB Gain Compression Point 

• Minimum noise fgure for sensitive measurements 

• Output power level needs to be controlled to not compress the signal analyzer 

• Need to maximize dynamic range 

• Survive maximal signal levels 

• Filter external signals outside of the CBRS band 

• Variable distance between preselector and signal processing unit 

The constraints are balanced by trading performance characteristics such as gain, compression (linearity), noise fgure maintenance, and signal level. Initially, this design is accomplished by cascading system components utilizing 

information provided by component data sheets, and the predicted performance relies on this information. Cascaded 

analysis provides insight into internal signal characteristics with overall performance in mind. 

In a system cascade, the noise fgure is dominated by initial components that are in front of the frst LNA gain stage. 

The design of the preselector includes a calibration switch and preselection flter. Additionally, a power limiter has 

been included to provide protection to the LNA in the event of a large signal being present at the input. The design of 

the signal processing unit with the added LNA is such that a fxed loss can be accommodated between the preselector 

and signal processing unit. This enables a long cable to be placed between the preselector and the signal processing 

unit for fexibility in installation locations. An added attenuator is included in the signal processing unit to ensure the 

LNA and signal analyzer do not compress the signal. A full system block diagram is shown in Fig. 4.11. The gain of 

full system is 32.1 dB and the noise fgure is 4.9 dB as shown in Fig. 4.12. The dominant factor in this overall noise 

fgure is due to loss from the components before the LNA stage. 

Finally, utilizing the cascaded gain and noise fgure for the system is shown versus frequency in Fig. 4.14 demonstrating performance across the entire CBRS band. 

4.2.3 On-board Calibration 

On-board calibration is integrated into the measurement system. A NIST-traceable noise diode (NSR) is integrated 

into the front of of the measurement system. The purpose of the calibration is to minimize the uncertainty of the 

measurement. This allows the system to compensate for system variations of components over time. An example 

27 
DRAFT 

Figure 4.11: Full system cascade block diagram illustrating the components and interconnects that are considered in 

estimating the cascaded gain and noise fgure. 

Figure 4.12: Cascaded gain (top) and noise fgure (bottom) of the full system showing the contribution of each component to the overall system gain of 32.1 dB and noise fgure of 4.9 dB. 

of this effect is shown in the NTIA TR-20-548 [12]. In this report, the gain of the measurement sensor shown in 

Figure 4.15, referred to as the preselector, shows gain drift over a multi-year period. The gain of the system lowers as 

1155 a function of time. These macro changes, as well as diurnal changes due to temperature variation, can be accounted 

for by leveraging an on-board calibration source. 

After bench and initial feld calibrations described in Section 7, this calibration with the on-board noise diode will occur 

periodically. Initially, it will occur at 60 minute intervals. However, as calibration actions detract from measurement 

time, the calibration variability will be analyzed and the frequency of on-board calibrations may be reduced in the 

1160 future. 

28 
DRAFT

Figure 4.13: Cascaded signal level and compression characteristics of the full system when driven at the P1dB compression point of -18.32 dBm. This demonstrates how the signal compares to the compression point at each point in 

the system. 

Figure 4.14: Cascaded gain and noise fgure prediction of the system over the entire CBRS band. 

29 
DRAFT

Figure 4.15: Gain drift shown of measurement sensor leveraged in NTIA’s TR-20-548 Technical Report. 

30 
4.3 Sensor Implementation 

Since certain RF components need to be located near the antenna to minimize system noise fgure, while other high 

size, weight and power (SWaP) components can be placed farther away, the system is partitioned into two main 

assemblies to add fexibility to support various feld installation requirements. These assemblies are the preselector 

1165 and the signal processing unit (SPU). The preselector assembly includes the RF components up to and including 

DRAFT

the LNA (designator AMP1 in Figure 4.2), along with supporting control and power distribution components. These 

components dissipate relatively little power and can tolerate an operating temperature of at least 50°C. As such, the 

preselector is assembled into a fully sealed weatherproof enclosure that can be mounted on a mast directly below the 

antenna. The SPU assembly, on the other hand, contains components which dissipate more power and are generally 

1170 larger and heavier. However, these components can be located farther from the antenna without sacrifcing overall 

system performance. Therefore, the current version of the SPU is built with the intention of being installed in a 

climate-controlled area where size and weight are not of primary concern, and can interface with the preselector via 

cables with lengths up to 30.5 m. 

Figure 4.16 is a high-level block diagram of the overall system as it has been implemented. In addition to the afore1175 mentioned preselector and the SPU assemblies, this diagram includes the commercial off-the-shelf (COTS) antenna

and long-term evolution (LTE) router, which enables remote access to the system. 

Figure 4.16: SEA Sensor Block Diagram 

The following sections provide further details on the preselector and SPU assemblies. 

4.3.1 Preselector Assembly 

The preselector assembly contains all RF system components up to and including the LNA (AMP1), along with 

1180 supporting power distribution, control and environmental monitoring components. A block diagram of the preselector 

is provided in Figure 4.17 and component part numbers are provided in Table 4.2. Note that Table 4.2 also includes 

component operating temperature ranges, which will be referenced in the following subsection. 

A network-enabled relay module in the preselector (reference designator CTL1 in Figure 4.17) allows the RF switch 

(SWT) state, power to the LNA (AMP1), and power to the noise source (NSR) to be controlled remotely. Overall 

1185 system power distribution is presented in more detail in Section 4.3.3. Additionally, the relay module supports the 

remote monitoring of external temperature and humidity sensors. Three sensors are installed in the preselector for 

environmental monitoring. The frst is a combined temperature and humidity sensor (DTS1) which is located in a 

central area of the preselector assembly. The other two sensors are temperature sensors (DTS2 and DTS3) which are 

directly attached to the LNA and noise source, respectively, using thermally-conductive epoxy. Monitoring of these 

31 
DRAFT 

1190 parameters is critical to verify functionality and integrity of the sensor system and support the credibility of the RF 

measurements made. 

Figure 4.17: Preselector Diagram 

The preselector is assembled in a 35.6 cm x 30.5 cm x 17.8 cm fberglass-reinforced polyester weatherproof enclosure 

with a National Electrical Manufacturer’s Association (NEMA) 4X / IP66 rating. This enclosure was selected to 

protect the internal electronic components from extreme environmental conditions including temperature, humidity, 

1195 and solar radiation. All RF and electrical components are mounted on an aluminum base plate and interconnected with 

coaxial and other control and power cables. A photo of the internal assembly is shown in Figure 4.18. The bottom 

side of the enclosure is modifed with a connector panel, shown in Figure 4.19, with feed-through connectors for RF 

input and output, 28 VDC power and Ethernet. An optional pole mount kit allows this enclosure to be mounted on a 

mast just below the antenna. 

1200 4.3.1.1 Preselector Thermal Testing 

Although the preselector components do not produce a signifcant amount of heat, they are sealed inside a waterproof, 

airtight enclosure with no active cooling. As such, thermal analysis was performed to calculate the expected air 

temperatures inside the enclosure at the maximum power dissipation of 5.64 W and worldwide maximum ambient 

design temperature of 49°C. Under these conditions, the model predicted maximum air temperatures just above the 

1205 components of 7.7°C without solar radiation and 10.5°C with maximum solar radiation. Figure 4.20 shows the 

32 
Table 4.2: Preselector Components 

DRAFT 

Designator Description Manufacturer Part Number Temperature Range 

AMP1 Low noise amplifer, 

200 MHz - 4 GHz LiConn LNA02004000A -40 to +85°C 

BPF Bandpass flter, 

3550-3700 MHz K&L Microwave 13FV40-3625/ U150-O/O -40 to +85°C 

CTL1 Network-enabled controller with 

4 relays and 4 digital inputs ControlByWeb X410-I -40 to +80°C 

DTS1 Digital temperature and 

humidity sensor ControlByWeb X-DTHS-P -40 to +80°C 

DTS2, DTS3 Digital temperature sensor ControlByWeb X-DTS-U -55 to +125°C 

ISL Isolator, 

2-4 GHz CentricRF CI2040 -40 to +70°C 

LMT Limiter, 

20 MHz - 4 GHz Fairview Microwave FMLM2003 0 to +50°C 

MCS Magnetic contactclosure sensor ControlByWeb MPS -40 to +100°C 

NSR Noise source, 

10 MHz to 18 GHz Keysight 346B 0 to +55°C 

PWR1 Power supply, DC-DC converter, 

28 VDC input, 5 VDC output Acopian 28EB5E250 -20 to +71°C 

SWT Coaxial switch, single-pole 

double-throw DC-18 GHz Dow-Key 401-6308 -25 to +65°C 

Figure 4.18: Preselector Assembly 

33 
DRAFT

Figure 4.19: Preselector Connector Panel 

predicted air temperature just above the components with and without solar radiation. Note that the intended areas of 

deployment do not experience ambient temperatures that high. 

Figure 4.20: Thermal Analysis Results Without Solar Radiation and With Solar Radiation 

To verify the thermal model, a preselector was installed on the roof of E Building at MITRE in Bedford, MA on 7 July 

2022. Internal enclosure temperatures were monitored using three temperature sensors located inside the enclosure. 

The following are the locations of the sensors, as depicted in Figure 4.18 

• Near center of assembly, mounted to the baseplate (DTS1) 

• LNA component body (DTS2) 

• Noise source component body (DTS3) 

An additional sensor monitored external air temperature. However, the external sensor malfunctioned, so weather 

history recorded hourly at Hanscom Air Force Base in Bedford, MA during the day of the test was used instead. The 

enclosure was exposed to full sun for the entire test. Figure 4.21 shows the recorded temperatures. 

1210 

1215 

The test started at 14:34 EDT when the enclosure door was sealed and the equipment was confgured to consume its 

maximum power. Data was recorded for just over 3 hours. Ambient temperature peaked at 27.8°C at 15:51 EDT. The 

component and internal ambient temperatures peaked at about 16:32 EDT, giving a maximum rise over ambient of 

1220 8.3°C during the test. 

34 
DRAFT Figure 4.21: Preselector Outdoor Thermal Test Results 

These results provide confdence that the equipment will operate outdoors at the maximum expected ambient temperature and exposed to solar radiation without foreseeable issues. Table 4.2 shows the operating temperature range for all 

components in the preselector. The component with the lowest operating temperature limit is the RF limiter (designator LMT), with a maximum operating temperature of 50°C. Considering the maximum rise over ambient temperature 

1225 observed during the roof testing of 8.3°C, the preselector is expected to operate within temperature specifcations up 

to an external temperature of 41.7°C. 

4.3.2 SPU Assembly 

The SPU assembly contains the remaining RF components which are not built into the preselector, namely the attenuator (ATT), second amplifer (AMP2) and RSA, as well as the embedded computer (CMP), and various system 

1230 control, power management and networking components. A block diagram of the SPU is provided in Figure 4.22 and 

component part numbers are provided in Table 4.3. 

The power management components in the RSA generate and distribute power to the entire sensor, including both 

internal and external components. The overall system power distribution, including these components inside the SPU, 

is presented in more detail in Section 4.3.3. 

1235 As with the preselector, a network-enabled relay module in the SPU (reference designator CTL2) serves a dual purpose 

of enabling or disabling DC power to various components and also remote monitoring of temperature and humidity. 

Two sensors are installed in the SPU assembly for this purpose. The frst sensor is a combined temperature and 

humidity sensor (DTS4) located in the general vicinity of the computer and power distribution components. The 

second sensor is a temperature sensor (DTS5) located near the RSA. 

1240 Finally, a network switch in the SPU (reference designator NSW) creates an local area network (LAN) which enables 

35 
DRAFT 

communication among all network-enabled components in the sensor. Specifcally, these components are the networkenabled controllers in the preselector and SPU (reference designators CTL1 and CTL2, respectively), the networkenabled remote power switch (CTL3), the embedded computer (CMP), and LTE router. 

Figure 4.22: SPU Diagram 

The current version of the SPU is shown in Figure 4.23. The SPU consists of several 19" rack-mount components 

1245 and sub-assemblies installed into a transit case. The individual components and sub-assemblies are annotated in the 

fgure. Starting from the top is the 1U WebSwitch module (CTL3), which is used to control AC power to the RSA 

and computer (CMP). Next are two custom-designed 2U trays, the RF Tray and the Power/Control Tray, separated by 

1U of open space for ventilation. Next is a 2U storage drawer, and fnally the 1U uninterruptible power supply (UPS) 

(PWR3). Note that a 7U variant of this design was also built, which is identical to the 9U shown in Figure 4.23 except 

1250 that it does not have the 2U storage drawer. 

36 
DRAFT 

Table 4.3: SPU Components 

Designator Description Manufacturer Part Number 

AMP2 Low noise amplifer, 500 MHz - 8 GHz Mini-Circuits ZX60-83LN-S+ 

ATT Attenuator, 6 dB, DC - 18 GHz Mini-Circuits BW-S6W2+ 

CMP 

Small form factor computer, 

11th Gen Intel® CoreTM i7 processor, 

32 GB RAM 

SimplyNUC NUC11TNHI7Z 

CTL2 Network-enabled controller with 

4 relays and 4 digital inputs ControlByWeb X410-I 

CTL3 WebSwitch Plus network-enabled remote 

power switch and auto reboot device ControlByWeb XRDI-WS3P-NA 

DTS4 Digital temperature and humidity 

sensor ControlByWeb X-DTHS-P 

DTS5 Digital temperature sensor ControlByWeb X-DTS-S3C 

NSW Ethernet switch, Gigabit, 8-port TRENDnet TI-G80 

PWR2 Power supply, DC-DC converter, 

28 VDC input, 5 VDC output Acopian 28EB5E250 

PWR3 Uninterruptible power supply, 

1440 VA, 1100W Eaton 5P1500R 

PWR4 Power supply, AC-DC converter, 

100 - 264 VAC input, 28 VDC output SL Power Electronics HC28-2-A+G 

RSA Portable USB real-time spectrum analyzer, 

9 kHz to 18 GHz Textronix RSA507A 

The Power/Control Tray, shown in Figure 4.24, houses the following components. Note that the reference designators 

provided in the list below can be cross-referenced to Table 4.3 to obtain full part numbers. 

• Ethernet switch (NSW) 

• Small form factor computer with power adapter (CMP) 

1255 • 28 VDC power supply (PWR2) 

• Network-enabled controller (CTL2) 

On the front panel of the Power/Control Tray are connectors used for the required external interfaces. These include 

power and Ethernet interfaces to the preselector and LTE router, power and USB interfaces to the RF Tray, and external 

connections to the computer for an optional keyboard, mouse or HDMI display. The rear panel of the Power/Control 

1260 Tray has two connectors for AC power inputs for the computer and the 28 VDC power supply, additional USB and 

Ethernet connectors, and a temperature sensor interface to the RF Tray. The Power/Control Tray front and rear panels 

are shown in Figure 4.25 and Figure 4.26, respectively. 

The RF Tray, shown in Figure 4.27, houses the following components: 

• Low noise amplifer (AMP2) 

1265 • Attenuator (ATT) 

• 5V DC-DC converter (PWR2) 

• Real-time spectrum analyzer with power adapter (RSA) 

On the front panel of the RF tray is a type N connector that interfaces with the coaxial cable from the preselector, along 

with USB and DC power connectors that interface with the Power/Control Tray. On the rear panel is an AC power 

1270 input connector which supplies power to the signal analyzer, as well as a temperature sensor interface connector to the 

37 
DRAFT Figure 4.23: SPU Assembly (9U) 

Power/Control Tray. The RF Tray front and rear panels are shown in Figure 4.28 and Figure 4.29, respectively. 

Both the Power/Control Tray and the RF Tray are installed in the transit case with their tops open (i.e. no top covers 

installed) to allow adequate airfow and heat dissipation. The SPU must be operated in a climate-controlled area at 

typical ambient room temperature, and the protective front and back covers of the transit case must be removed. 

1275 4.3.3 System Power Distribution 

Figure 4.30 is a diagram of the power distribution for the overall SEA sensor system. The system is powered by 

standard 120 VAC line power. Main power enters the system through the UPS (PWR3) located in the SPU. The 

UPS is capable of supplying backup power to the system for an estimated 60 minutes in the event of a power outage. 

From here, AC power is routed to a 28 VDC power supply (PWR4) and to a "WebSwitch" network-enabled relay 

1280 module (CTL3). The WebSwitch is used to distribute AC power to the computer (CMP) and signal analyzer (RSA). 

Distributing power to these components through the WebSwitch enables a hard reset to be executed in the event of a 

system malfunction. 

28 VDC power in the SPU is distributed to the LTE modem, network switch (NSW), and a second network-enabled 

relay module (CTL2). Controller CTL2 subsequently distributes 28 VDC to the DC-DC converter (PWR2) for the 

1285 second LNA (AMP2), the preselector, and an auxiliary 28 VDC output accessed via the front panel. Relays in the 

X410 allow power to any of these outputs to be remotely controlled. 

28 VDC power in the preselector is then distributed through another network-enabled relay module (CTL1) to the noise 

source (NSR) and the DC-DC converter (PWR) for the LNA (AMP1). By distributing power to these components via 

38 
DRAFT Figure 4.24: SPU Power Control Tray 

Figure 4.25: SPU Power Control Tray Front Panel 

Figure 4.26: SPU Power Control Tray Rear Panel 

the relay module, power can be remotely enabled or disabled. The relay module also distributes 28 VDC to the RF 

1290 switch (SWT). This serves as the control voltage for the switch, enabling the controller to set the switch state and

select the RF input to the preselector. 

4.3.4 LTE Router 

Without guaranteed hardline connectivity to provide backhaul of sensor data, a wireless backhaul strategy is employed. 

For this, cellular routers are used as these provided the highest chance of coverage for the sensors in a variety of 

39 
DRAFT Figure 4.27: SPU RF Tray 

Figure 4.28: SPU RF Tray Front Panel 

Figure 4.29: SPU RF Tray Rear Panel 

1295 locations. A Digi IX20 LTE Router was chosen to enable remote access to the sensor data products as well as remote

monitoring and administration of the sensor. This router provides the needed connectivity and also provides the 

router administration needed to provide connectivity to the individual networked sensor components (web switch, host 

processor, etc.). Finally, these routers are capable of being linked in a private network linked back to the management 

software and network. 

40 
1300

1305

1310

1315

1320

DRAFT Figure 4.30: Power Distribution Diagram 

4.3.5 Future SPU Enhancements 

The Signal Processing Unit is currently in a fnal prototype version (Prototype Rev 3). National Advanced Spectrum 

and Communications Test Network (NASCTN) plans on design enhancements rolled into subsequent versions. The 

following is a summary of the current version and what is expected in NASCTN SEA sensor production versions. 

• Prototype Rev 3 Version SPU: A ruggedized 19 inch rack enclosure (based on a 7U or 9U Pelican BlackBox) 

was utilized as the enclosure for this version. It is comprised of several trays containing the power, control and 

RF components. This was chosen for quick prototype deployment to support initial sheltered, climate controlled 

installations. This arrangement was chosen so that hardware and system changes can be more easily implemented 

as the initial design is tested and bench-marked. Digital, control, and power components are distributed across a 1U 

space (for the Webswitch) and in a 2U tray near the center of the stackup. RF components are located in the 2U 

RF tray is located just below the Webswitch space. The SPU has some limitations with its weight, size, and being 

essentially a laboratory grade system. To expand installation site options and include outdoor non-sheltered areas, 

there is a need to update the SPU design to overcome the environmental limitations, optimize its weight and size, 

and improve its performance. This will require an analysis of the sensors heat producing components (Computer, 

Spectrum Analyzer, etc) in a real world installation to estimate the amount of cooling required and an optimum 

component layout. Additionally, considerations on removal of humidity and SPU EMI emissions will be worked in 

parallel with this. The next revision (Rev 1/2) will address all these limitations. 

• Rev 1 & 2 SPU: These future versions will incorporate various enhancements including a revised enclosure for outdoor installations featuring a sealed electronics cavity with active cooling and humidity control. This will facilitate 

installation in both indoors or in weather exposed outdoor environments with rain, temperature, and solar radiation. 

It will also feature an optimized form factor to minimize size and weight for easier installation. 

41 
DRAFT 

Table 4.4: Sensor RF Specifcation Summary 

Parameter Typical Value Conditions Reference 

System 

Frequency Range 3550-3700 MHz 

Section 4.4.1 

Gain 31.5 dB Channel 8 (3620-3630 MHz) 

RSA preamplifer enabled 

RSA reference level = -25 dBm 

RSA input attenuator = 0 dB 

Input-Referred Noise Power -98 dBm/10 MHz Section 4.4.2 

Input 1 dB Compression Point 

(with CW Stimulus) -48 dBm Section 4.4.3 

Antenna

Gain 9 dBi 

Polarization Vertical Section 4.2.1.1 

Vertical 3 dB Beamwidth 11° 

1325 

1330 

1335 

1340 

1345 

1350 

4.4 Full-System RF Measurements 

This section provides details on several RF measurements that were made on the fully assembled SEA sensor. Unless 

otherwise noted the measurements encompassed the entire RF system as shown in Figure 4.2 up to and including 

the RSA, but excluding the antenna. Since measurements were done on the fully assembled system, they include the 

impact of many small insertion losses resulting from various interconnects and front panel connectors not captured 

in the RF system diagram. A summary of RF performance specifcations is provided in Table 4.4, and further details 

on these measurements are discussed in the subsections that follow. The system under test only deviated from the 

deployed sensors in that the 30.5 m coaxial cable between the preselector and SPU was not used. In lieu of the long 

coaxial cable, a short cable and attenuator with a combined insertion loss of 8.5 dB was used. This is nearly equal to 

the measured insertion loss of the long cable of 8.4 dB. 

4.4.1 Gain and Frequency Response 

The gain of the full system was measured using a signal generator to source a CW reference signal to the input of the 

preselector, and recording the signal level measured by the RSA. The measurement was automated so that it could be 

performed over a range of frequencies to characterize the system frequency response. A Rohde & Schwarz SMB100A 

microwave signal generator was used to sweep a signal over the frequency range 3375 MHz to 3875 MHz in 1 MHz 

steps. At each frequency step, the spectrum trace was acquired from the RSA and the measured signal level at the 

source frequency was recorded. The measured signal level and known power level of the CW input signal were then 

used to determine the system gain. The result is shown in Figure 4.31. This measurement shows the total system gain 

within the CBRS band to be approximately 30.5 dB to 31.5 dB. Outside of the CBRS band the gain rolls off closely 

following the response characteristics of the bandpass flter (BPF), as detailed in Table 4.1. It is important to note that 

this gain measurement relies on the signal level as reported by the RSA, which has a specifed amplitude accuracy of 

± 1.75 dB for the frequency range at which this test is performed. 

4.4.2 Noise Figure 

The system noise fgure was measured at National Institute of Standards and Technology (NIST) following the Yfactor technique. A calibrated noise diode (NIST ID = D14B2.089, MY6140089) with an excess noise ratio (ENR) 

of 14.91 ± 0.05 dB was used for this measurement. The noise diode was connected to the preselector input using a 

3.5 mm-SMA adapter followed by an SMA-N adapter. The RSA was confgured to replicate the deployed sensors, 

with the preamplifer enabled, reference level set to -25 dBm and input attenuator set to 0 dB. 

With a sample rate of 14 mega samples per second (Msps) (corresponding to a usable bandwidth of 10 MHz) IQ data 

was acquired from the RSA with the noise diode both enabled and disabled. From these IQ time series the mean 

power with the noise diode enabled and disabled were computed, and subsequently the Y-factor and noise fgure were 

42 
DRAFT

4.32. 

Figure 4.31: Full System Frequency Response 

calculated. This measurement was repeated at 15 frequencies across the CBRS band, the result is shown in Figure 

Figure 4.32: Full System Noise Figure 

4.4.3 Gain compression 

1355 The input 1 dB compression point of the system was measured using a Rohde & Schwarz SMB100A microwave signal 

generator to source a CW signal at increasing power levels to the input of the preselector. At each input power step 

the signal level reported by the RSA was used to determine system gain. As the input power was increased, the point 

where the measured gain deviated from a constant gain by 1 dB was recorded. 

43 
DRAFT 

This measurement was performed with an input frequency of 3625 MHz. The RSA preamplifer was enabled, a range 

1360 of reference level settings were used, and the input attenuator was either set to "auto" or manually set to 0 dB. The 

result is shown in Figure 4.33. The deployed sensors are currently confgured for a fxed reference level of -25 dBm and 

input attenuator of 0 dB. Under these conditions, the input 1 dB compression point was determined to be -47.7 dBm. 

Figure 4.33: Full System 1 dB Compression Point 

For most reference level settings measured, the gain remained nearly constant up until the point that the RSA reported 

an "ADC overrange" condition. Figure 4.9, although a measurement on the RSA only and not the full system, is 

1365 representative of this type of gain compression characteristic. This indicates that the RSA, and not the RF amplifers 

in system, is limiting the system 1 dB compression point. Only at the highest reference level measured, +10 dBm, and 

with the input power level greater than -15 dBm was compression of the RF amplifers observed. 

4.4.4 Sensor Overload 

In addition to overload/compression measurements performed on the signal analyzer itself, an overload analysis was 

1370 performed on the sensor system to observe the effects on adjacent channel measurements. This was performed with 

two input signals: a CW tone and a Gaussian noise-type signal. First, a CW tone was placed at the antenna port on the 

preselector and increased in power until overload was detected. Three power levels were then tested to see the data 

product response to the stimulus. 

When the CW tone is well under the overload level as in Figure 4.34, only the channel containing the CW tone is 

1375 visible above the sensor noise foor. However, once the signal increases beyond the sensor overload, the power leaks 

into adjacent channels. In Figure 4.35, the CW was increased beyond the overload level and the power leaked into 

two adjacent channels above and below the main channel. This is consistent with the native sample rate of the RSA of 

56 Msps. I.E. 28 MHz above and below the center frequency corresponds to two 10 MHz channels above and below 

that are corrupted by an overload in the main channel. 

1380 In addition to a CW tone, a 10 MHz wide additive white Gaussian noise (AWGN) signal was used to mimic a wideband 

signal as a precursory stand-in for an allocation in the CBRS band. Similarly, when under the overload level, no 

channel bleed was observed and when the overload level was exceeded, channel bleeding was observed in the two 

channels above and below the channel containing the noise-like signal. The powers at which overload occurred 

corresponded to when the peak power of the noise-like signal exceeded the same power as a CW which caused 

44 
DRAFT

Figure 4.34: Channel power measurements with a CW stimulus well below the overload point. 

Figure 4.35: Channel power measurements with a CW stimulus above the overload level. 

1385 overload. However, unlike the CW case, when the noise-like signal power was increased further, channel bleeding 

into channels beyond the two channels above and below the main channel was observed as seen in Figure 4.36. 

45 
DRAFT

Figure 4.36: Channel power measurements with a noise-like stimulus greatly above the overload level. 

46 
DRAFT 

Chapter 5 

Sensor Management 

47 
5.1 Software 

1390 A distributed software system based on Institute of Electrical and Electronics Engineers (IEEE) 802.15.22.3-2020 [13] 

provides command and control over the network of sensors, and storage and retrieval for the data provided by the the 

sensors. As shown in Figure 5.1, the system is comprised of three main entities: an Ansible® control node, the sensor 

software, and the manager. Together, these entities provide automated provisioning, sensor control, and data access. 

DRAFT 

The Ansible® control node automates software provisioning. Ansible® playbooks install signal analyzer dependencies, 

Figure 5.1: A block diagram indicating the high-level software architecture. 

1395 confguration fles, sensor software, and apply the Department of Defense (DoD) Security Technical Implementation 

Guides (STIGs) [14] to secure the system. The sensor software, detailed in 5.1.1, establishes a common application 

programming interface (API) to interact with a sensor and the manager. Subsection 5.1.3 provides details on the 

manager software that provides command and control over the network of distributed sensor and storage and retrieval 

for sensor data. 

1400 5.1.1 Sensor

The sensor software was designed to provide easy-to-use remote sensor control and data retrieval regardless of the 

underlying hardware or sensing capabilities of the physical sensor. This is accomplished by abstracting sensing capabilities into named actions the sensor can perform. Actions may be parameterized so a single implementation may 

be instantiated into multiple actions. For example, Figure 5.2 provides an example confguration fle for an action to 

1405 acquire 10 MHz of in-phase and quadrature (IQ) data centered at 3555 MHz. There is a single software implementation of the IQ action, but duplicating this confguration fle with a different name and frequency would create a new 

IQ action at a new center frequency. 

Sensor control is accomplished through a web-based API that includes four key endpoints: 

• capabilities: Get requests provide a description of the physical components of the sensor and a list of the sensing 

1410 actions the sensor can perform. 

• schedule: Get requests provide a list of schedules including past, ongoing, and actions scheduled in the future. Put 

requests allow schedules to be modifed, including deactivated. Delete requests delete schedules and Post requests 

add new schedule entries. 

48 
DRAFT

Figure 5.2: An example confguration for the an action to acquire IQ data. 

• status: Get requests provide sensor status including scheduler status, location, system time, when the sensor soft1415 ware started, last calibration time, disk usage, how many days the sensor has been up, signal analyzer health status, and status of the preselector and switches. Preselector and switch statuses are driven by confguration fles 

and provide healthy Boolean, the name of the switch or preselector, and a true or false status for each confgured state indicating whether each state is enabled or disabled. The Sharing Ecosystem Assessment (SEA) sensor 

preselector is confgured to include noise_diode_powered, lna_powered, antenna_path_enabled, 

1420 and noise_diode_path_enabled states. The WebRelayTMlocated within the signal processing unit (SPU) includes rf_tray_powered, preselector_powered, and 28V_aux_powered states. The WebSwitchTMincludes 

sigan_powered and computer_powered states. 

• tasks: Get requests enable the retrieval of sensor acquisitions (signal metadata format (SigMF) archive of metadata 

and data). Delete requests delete the data and metadata. 

1425 As shown in Figure 5.3, the web services are implemented as Django applications hosted in a Gunicorn application 

server and exposed via NGINX, a high performance web server. For portability and ease of deployment, the sensor 

software is containerized with Docker. NGINX, the PostgreSQL database, and Gunicorn along with the sensor software are each hosted in separate containers. In addition, there is a fourth Autoheal container, described further in 

section 5.1.4 that monitors the health of the API container and restarts it, if necessary. 

Figure 5.3: A block diagram indicating the sensor software architecture. 

1430 5.1.2 Sensor Actions 

Two actions were added to enable long-term continuous monitoring within the Citizens Broadband Radio Service 

(CBRS) band. A calibration action, detailed in section 7.1.2.1, supports periodic calibration. In addition, a single data 

49 
DRAFT 

product action creates the data products described in section 6. 

5.1.3 Manager 

1435 The manager provides the following key capabilities: 

• Authentication and Authorization: Personal Identity Verifcation (PIV)/Common Access Card (CAC) based authentication and authorization ensures only authorized users can access the network of sensors and any data provided by 

the sensors. 

• Sensor Registration: Authorized users may register and remove sensors from the system. Each sensor requires a 

1440 unique digital certifcate that identifes the device and and all communication between the sensors and manager 

occur with secure two-way authentication. 

• Capabilities Discovery: the sensor network may consist of heterogeneous sensors that may differ in underlying hardware and/or software capabilities. The manager allows users to interrogate sensors to discover hardware components 

and sensing capabilities. 

1445 • Scheduling: RF sensing capabilities are defned as distinct tasks known as actions. Actions can be scheduled on one 

or more sensors. A schedule includes an action and name, and may include a start time, stop time, and interval that 

defnes the amount of time to wait between action executions. 

• Data Storage and Retrieval: When a sensor executes an action it produces an acquisition, or a combination of 

metadata and data, and notifes the manager that new data has been acquired. The manager retrieves the data and 

1450 deletes it from the sensor. All data residing within the manager may be searched for and retrieved via the Web-based 

graphical user interface (GUI). 

• Analytics and Visualization: a well defned analytics API supports integration of custom batch and streaming analytics. 

As shown in Figure 5.4, manager uses a layered architecture with well defned services that provide these capabilities. 

Figure 5.4: The high level software architecture diagram of the manager and its key services. 

50 
DRAFT 

1455 Each service implements an interface and components within the system only interact with these interfaces. In addition, service implementations are specifed in confguration fles and supplied to the system through dependency 

injection. 

5.1.4 Failure Recovery 

The sensor and manager software are both designed to recover from known failure conditions. The sensor software 

1460 uses optional settings to indicate the WebSwitchTMand states defned in the WebSwitchTMto use to power cycle the 

signal analyzer. When the API Docker container starts, the sensor software connects to the signal analyzer. If any 

errors occur, the sensor software will power cycle the signal analyzer and reconnect. In addition, the sensor software 

employs a separate Autoheal Docker container to monitor the health of the API container. If the API container is 

unhealthy the Autoheal container will restart the API container. Upon restart, the API container may power cycle the 

1465 signal analyzer as detailed above. At the start of each sensing action, the sensor software performs a health check. If 

the health check fails, the API container is marked as unhealthy. 

The sensor and manager software also account for network disruptions. Every acquisition performed by the sensor is stored locally on the sensor and the sensor notifes the manager that a new acquisition is available. If the 

sensor is unable to notify the manager it marks the acquisition status as notification_failed. The man1470 ager uses a confgurable background thread to check the sensors in any active schedules for any acquisitions with a 

notification_failed status and download and delete any that it discovers. 

5.1.5 System Monitoring and Alerting 

The data product action, detailed in 6, provides a variety of metadata such as temperature, humidity, and disk usage 

used to monitor the health and status of the network of sensors. The manager’s analytic API allows plugins to subscribe 

1475 to data provided by specifc sensors and actions. A monitoring and alerting plugin subscribes to acquisitions of the data 

product to provide alerts if acquisitions are no longer received from a sensor or environmental conditions approach the 

limits of the sensor. 

51 
DRAFT 

5.2 Network and Data Backhaul 

For backhaul connectivity, each sensor is equipped with a long-term evolution (LTE) router. These routers are confg1480 ured into a private LTE network with one central router bridging to the Institute for Telecommunication Sciences (ITS) 

network where the necessary services such as spectrum-characterization and occupancy sensing (SCOS) manager reside. 

The local sensor network is managed by the local LTE router. This router handles the port forwarding necessary to 

branch out the same service to each device (e.g. both web relays use the same ports for their services). 

1485 This router also is part of the private LTE network with its own LTE internet protocol (IP), along with the routers from 

each other sensor. An additional LTE router is confgured on the ITS network with routing rules and port openings to 

allow traffc to pass through the two networks. 

Each LTE router is on a shared, data-limited plan with the limit estimated by double the estimated data accumulation 

(since the data is both uploaded by the sensor router and downloaded by the ITS router). 

Figure 5.5: A network diagram of the connectivity between the manager and sensors. 

52 
DRAFT 

1490 5.3 Data Repository 

The data products described in 6 are contained in SigMF [15] archives. Each SigMF archive has one or more recordings, and a recording includes a JavaScript object notation (JSON) metadata fle and a dataset fle containing the radio 

frequency (RF) measurement data. 

1495 

database. 

All recordings produced by the sensors are stored in the data repository. The data repository is encapsulated within 

manager’s data service. This provides the opportunity to change the details of the underlying repository with minimal 

impact on the rest of the system. The data service uses a combination of database and fle system storage to store the 

metadata and data provided from the sensors. As shown in 5.6, a minimal subset of metadata is stored in a Structured 

Query Language (SQL) Server database to facilitate search and retrieval of acquisitions provided by the sensors. The 

full SigMF metadata fle and the data fle are stored on disk at the location specifed by the flepath column in the 

Figure 5.6: The database table for acquisitions. 

1500 

53 
1505

1510

1515

1520

1525

1530

DRAFT 

Chapter 6 

Sensor Data 

This chapter addresses the real-time acquisition and analysis of radio channel measurements performed by each sensor 

in CBRS channels. 

6.1 Edge Data Payloads 

Deployed SEA sensors produce in-situ measurements relayed to a central repository. The relayed measurements are 

referred to as edge data payloads. These data comprise a stream of power statistics and their distributions across time 

and frequency, together with associated metadata. These measurements serve as low-level building blocks to support 

analysis needed for the National Advanced Spectrum and Communications Test Network (NASCTN) SEA project. As 

such, a subset of the sensor data will be used in post-analysis to produce derived measurements that address the basic 

test questions. Edge data payloads are thus distinguished both from the lowest level data raw signal analyzer output 

(baseband waveforms) and from higher-level data products developed from post-analysis of aggregated payloads. 

Each sensor must be automated to operate independently as a feld instrument that is tailored to assess the CBRS 

sharing environment. This operation requires the development of automation software that is responsible for operating 

the signal analyzer, digital signal fltering, statistical analysis, and integration with the SCOS software framework. By 

orchestrating these functions, the sensors acquire waveforms from CBRS channels, apply calibration corrections to the 

data, and record the end results. The measurement traces from each sweep are packaged with metadata by the SCOS 

software for upload to centralized data storage hosted by NASCTN. 

This section summarizes the design, implementation, and performance of reference software implementation of the 

above CBRS sensing requirements. It has been tested in the feld through deployment in 3 sites. If needed, the 

confguration and implementation can be adapted rapidly to accommodate changing requirements in the future. As 

such, the reference sensing capability can be interpreted as an implementation template for the feld assessments 

described in the test plan. 

6.1.1 Reference Architecture 

The sensor performs a stepped frequency sweep across the CBRS band, channelized into 10 MHz steps. In each 

channel, the sensor computes statistics on RF power based on IQ waveforms streamed from its signal analyzer. Within 

practical data and computation constraints, the goal is to generate a rich data payload which allows for fexible and 

in-depth analysis in aggregate. 

Overall design of the edge-computed signal processing algorithm is driven by project goals subsection 1.1.3 and 

operating parameters of CBRS but constrained by practical implementation limitations. For example, a key goal of the 

project is to assess the vacate time of measured Citizens Broadband Radio Service devices (CBSDs) in the presence of 

a protected incumbent. There is also a maximum data throughput rate supported by the LTE backhaul, which limits the 

amount of data which can be sent from sensors at the edge back to the central NASCTN data repository. Finally, there 

are some limitations present due to the constraints of the SPU computer, such as processing and memory capacity. 

54 
1535

1540

1545

1550

1555

1560

DRAFT

Capture 

1

IQ Captur es

IQ Pr ocessing

Capture 

2

Capture 

3

Process 2

Process 3

... Capture 

15

Process 1

...

Process 15

Generate 

metadata, 

compress 

data 

product, 

save file

NASCTN SEA

Edge Compute Process Diagram

Prototype v0.2

~75 seconds to measure the 

entire CBRS band

Data size: 100 kB

(~3 GB monthly, 

per sensor)

Figure 6.1: A block diagram indicating the high-level processing fow of IQ captures and data processing for a fullband edge computed data capture. 

Figure 6.1 shows a high-level overview of the edge compute algorithm’s processing fow and parallelization. The 

sensors run two primary processes in parallel: acquisition of IQ waveform captures (the blue block), and IQ sample 

post-processing (the green block). After the sensor records IQ samples in one channel, the post-processing of those 

samples begins concurrently with the next channel IQ recording. Finally, after all IQ recording and processing has 

been completed, capture metadata and sensor diagnostics are recorded and the results are saved (the red block). On 

average, the combination of these processes takes approximately 5 s in total to complete for a single 10 MHz channel 

using a confgurable channel dwell time initially set to 4 s, with a measurement of the full CBRS band being performed 

in approximately 75 s. In the current confguration a channel revisit time is approximately every 90 s. The timing can 

be specifed to be on an exact cadence or set to initiate as soon as the prior compute action has completed. The current 

confguration is a baseline from which to work off and is subject to change based on long-term analysis trends.The 

total recording and processing is ultimately a function of the IQ recording dwell time in each channel. The dwell time 

itself sets a lower bound on total process time. Longer channel dwell times also result in more samples being processed 

into power statistics, and the computation time of these processes increases with the number of input samples. 

Figure 6.2 shows the IQ post-processing which occurs for each channel after recording has completed. The IQ postprocessing for each channel is implemented using a combination of serial and parallel tasks. First, the infnite impulseresponse (IIR) flter is applied to the IQ waveform. Next, parallel processes compute each of power spectral density 

(PSD), time series power, amplitude probability distribution (APD), and periodic frame power (PFP) results. Each of 

these steps is described in detail below. 

6.1.2 Real-Time Waveform Streaming 

Working within these constraints, a signal processing algorithm was designed, to be computed at the edge on the SPU. 

This algorithm produces the following power statistics for the IQ waveform acquired in each channel and sweep: time 

series, power spectral density (PSD), periodic frame power (PFP), and amplitude probability distribution (APD). IIR 

fltering is used to constrain all of these power statistics to each channel. Each component of the sensor data stream, 

and the processing fow of the algorithm overall, are described below. 

6.1.2.1 Acquisition 

First, the signal analyzer is used to capture IQ samples. The analyzer is set to use a 14 MHz sampling rate, which 

results in 10 MHz of valid instantaneous measurement bandwidth due to the anti-aliasing flters built in to the signal 

analyzer itself. The center frequency is set to 3555 MHz, and a 4 s IQ capture is performed which instantaneously 

55 
DRAFT

Category Parameter Value 

Record time 4 s 

Center Frequency 3555, 3565, . . . , 3695 MHz 

IQ Capture and signal analyzer Preamp Enable True 

Reference Level -25 dBm 

Sampling Rate 14 MHz 

Max. Passband Ripple 0.1 dB 

IIR Filter Min. Stopband Attenuation 40.0 dB 

Passband Edge Frequency 5 MHz 

Stopband Edge Frequency 5.008 MHz 

Time Domain Power Series Detector Window 10 ms 

discrete Fourier transform (DFT) Length 875 

PSD 

Number of DFTs for Detection 64,000 

Window Type Flat top 

Frequency Resolution ∆f 16 kHz 

PFP 

Reference Frame Period 10 ms 

Resolution 1/56 ms (≈ 17.9 μs) 

APD Power Bin Size 1 dBm 

IQ Data Power Spectr al Density (PSD)

1250 Samples

IIR Filter ing Tim e Dom ain Power Statistics

800 Samples

Am plitude Pr obability 

Distr ibution (APD)

Variable number of samples, 

approximately 250

Per iodic Fr am e Power (PFP)

3360 Samples

Single Channel Data Result

NASCTN SEA

Edge Compute Algorithm 

Over view - Single Channel

Prototype v0.2

Tim e Dom ain Power Sum m ar y 

Statistics

2 Samples

Figure 6.2: A block diagram indicating the digital signal processing fow for a single channel IQ capture within the 

edge compute algorithm. The IQ samples are fltered and then processed in parallel to generate the various components 

of the edge data payload. 

Table 6.1: An overview of edge compute parameters used for prototype IQ capture and data processing. 

56 
DRAFT 

Geff, refer to section 7.1.2.1. 

IQ[n] = I[n]

√Geff 

Q[n] + j √Geff 

(6.2) 

6.1.2.3 Digital Filtering 

1565 

1570 

1575 

1580 

1585 

1590 

1595 

measures the entire 10 MHz channel from 3550 to 3560 MHz. The attenuation setting on the signal analyzer is variable 

dependent on the sensor location and the strength of signals observed, with the goal being to use as little attenuation as 

possible while avoiding signal analyzer overload on any channel. A single channel IQ capture results in a discrete-time 

sequence of complex-valued samples of a voltage waveform: 

IQ[n] = I[n] + jQ[n]. (6.1) 

Here, I[n] is the in-phase component and Q[n] is the quadrature component of the quadrature baseband signal. The 

sample index n ranges from 0 to 55,999,999 for a 4 s IQ capture using a 14 MHz sampling rate. 

6.1.2.2 Calibration Corrections 

Before any other stage of processing, the channelized IQ data is scaled using a gain factor obtained from calibrations, 

effectively changing the reference point of the data from the input of the signal analyzer up to the calibration point 

(near the antenna terminal). Y-factor calibrations using the on-board reference noise source (see Section 7.1.2.1) are 

automatically performed on a periodic basis in the feld after sensor deployment. This routine yields a calibrated power 

gain value for each channel. Note that actual signal gain is frequency-dependent, but the calibration routine generates 

a single calibrated gain value representing the entire 10 MHz channel. If the calibrated channel power gain, in linear 

units, is Geff, then the scaling is applied to recorded IQ samples by Equation 6.2. For details on the computation of 

Background: In earlier analysis stages of the signal analyzer response, it was shown that the integrated hardware 

anti-aliasing flters provide slow roll-off in frequency response outside of the measured channel. This presented a 

need for additional digital fltering of IQ samples, so that power statistics and other data results can be accurately and 

independently reported for each 10 MHz channel. Digital fltering improves the out-of-band rejection performance 

for each channel power measurement when the input power level is below the sensor overload level. 

Description: An IIR flter was designed which was optimized for flter stability, high passband fatness, high stopband 

rejection, and small transition band from passband to stopband. Due to the nature of the power statistics being reported, 

linear phase response was not an important consideration, so IIR fltering was chosen over fnite impulse response 

fltering. This results in faster and more effcient digital fltering of large streams of IQ samples at the edge, when 

compared to an equivalent fnite impulse-response (FIR) flter design. For comparison, a FIR flter designed using the 

Kaiser window method, with the same minimum stopband attenuation and transition width as the implemented IIR 

flter, would have 3909 taps. Implemented for comparison on a high-performance laptop, fltering a single 10 MHz 

channel IQ capture took 1.5 seconds using the IIR flter and 72.5 seconds using the comparable FIR flter. 

The IIR flter was designed and implemented as a digital Cauer flter of order 12. This elliptic flter type was chosen 

due to its ability to minimize the transition interval between the passband and stopband for a given amount of ripple 

[16]. The resulting flter has a maximum passband ripple of 0.1 dB below unity gain, minimum stopband attenuation 

of 40.0 dB, and a transition width of 8 kHz between the passband and stopband. With such a small passband ripple, 

the flter approximates the response of a Type II, or inverse, Chebyshev flter. The flter is implemented as a low-pass 

flter, which behaves like a band-pass flter for a complex input sequence such as the stream of input IQ samples. 

A low-pass flter with a 5 MHz cutoff frequency has 10 MHz bandwidth flter when applied to the double-sideband 

57 
DRAFT 

Table 6.2: The coeffcients of the IIR flter used in IQ data processing 

i ai bi

0 0.22001756 1 

1 1.89508588 5.98460684 

2 8.08369813 19.1994547 

3 22.28438409 40.7912472 

4 43.9358511 63.2429677 

5 65.02462875 74.3311099 

6 73.93117717 67.6982677 

7 65.02462875 47.8732528 

8 43.9358511 26.1496244 

9 22.28438409 10.7528549 

10 8.08369813 3.21640614 

11 1.89508588 0.636398683 

12 0.22001756 0.0740808688 

complex baseband signal. The flter was analyzed in the Z-plane and found to be stable since its poles lie within the 

1600 unit circle. To avoid numerical errors and potential reduced flter stability due to fnite foating point precision, the 

flter is implemented using second-order sections. The flter’s transfer function is defned to be: 

P12 biz−i

H i=0 (z) = P12 −j (6.3) 

j=0 aj z

The flter coeffcients are given in Table 6.2. The flter’s amplitude and phase responses are shown up to the Nyquist 

frequency in Figure 6.3. Additionally, the amplitude and phase responses are shown over smaller frequency spans near 

the transition region in Figures 6.4 and 6.5. The transfer function is represented in the Z-plane using a pole-zero plot 

1605 in Figure 6.6. Finally, the flter’s step response is shown in Figure 6.7. 

Uses: The fltering stage is performed after IQ samples are scaled for calibrated sensor gain, and before all other 

subsequent, parallelized processing stages. Therefore, the benefts of limiting the measurement bandwidth to the 

10 MHz channel are carried on to each of the edge data payload components. 

58 
DRAFT

14

12

10

8

6

4

2

0

Phase Shift (rad)

0 1000 2000 3000 4000 5000 6000 7000

Frequency (kHz)

140

120

100

80

60

40

20

0

Gain (dB)

IIR Filter Response

Amplitude Response

Phase Response

Figure 6.3: The amplitude and phase response of the IIR flter, shown up to the Nyquist frequency. The amplitude 

response displays a sharp cutoff at 5 MHz. 

14

12

10

8

6

4

2

0

Phase Shift (rad)

4930 4940 4950 4960 4970 4980 4990 5000 5010

Frequency (kHz)

0.12

0.10

0.08

0.06

0.04

0.02

0.00

Gain (dB)

IIR Filter Response, Passband Edge

Amplitude Response

Phase Response

Maximum Ripple: -0.1 dB

Figure 6.4: The amplitude and phase response of the IIR flter, zoomed into the region from 4930 kHz to 5010 kHz to 

show the passband ripple. 

59 
DRAFT

14

12

10

8

6

4

2

0

Phase Shift (rad)

5000 5010 5020 5030 5040 5050

Frequency (kHz)

100

80

60

40

20

0

Gain (dB)

IIR Filter Response, Stopband Edge

Amplitude Response

Phase Response

Minimum Attenuation: -40.0 dB

Figure 6.5: The amplitude and phase response of the IIR flter, zoomed into the region from 4995 to 5055 kHz to show 

the transition and part of the stopband response. 

1.0 0.5 0.0 0.5 1.0

Real Part

1.00

0.75

0.50

0.25

0.00

0.25

0.50

0.75

1.00

Imaginary Part

IIR Filter Pole-Zero Map

Poles

Zeroes

Unit Circle

Figure 6.6: A pole-zero map, displaying the poles and zeroes of the IIR flter in the complex Z-plane. All of the poles 

lie within the unit circle, indicating flter stability. 

60 
DRAFT

0 1 2 3 4 5 6 7

Time ( s)

0.2

0.4

0.6

0.8

1.0

1.2

Amplitude

Step Response of IIR Filter

Figure 6.7: Plot showing step response of the IIR flter, demonstrating the initial overshoot and subsequent ringing 

effects. 

Calculation: The recorded IQ samples are fltered using the described IIR flter, implemented as a series of second1610 order flters with direct-form II transposed structure. This structure uses the minimum number of delay elements and 

multiplications, and implements the zeros before the poles. These qualities minimize numerical precision errors during 

fltering. 

6.1.3 Power Statistics 

6.1.3.1 Time-Series 

1615 Description: The channel power time-series quantifes the time-progression of mean and maximum channel power 

values across the duration of a waveform capture. In the reference implementation, peak and root mean square (RMS) 

power detectors are applied on short time-scales to give complementary insights about short bursts and average power, 

respectively. The time-resolution of the detectors, at 10 ms, is set to a single radio frame in the physical layer of 

cellular communication standards; insight on shorter time-scales is provided by the PFP, detailed in Section 6.1.3.3. 

1620 Background: This is a common type of measurement trace available on commercial test instruments, including signal 

analyzers and oscilloscopes. These instruments sometimes refer to this measurement as channel power, or power vs

time. 

Uses: The time-series can be used to identify the incident time and duration for exceptionally powerful signals present 

in the recorded waveform. Time-series traces of power can also be used to to compute summary statistics of the time1625 domain characteristics of each waveform capture. The linear power average, equivalent to the RMS power detection 

with bin size equal to the full duration of the waveform, is the mean of the RMS power trace. The instantaneous peak 

power of the capture is the maximum value of the peak power trace. These are useful metrics for quantifying average 

and instantaneous peak power recorded in the channel for comparison with CBRS protection thresholds. 

Calculation: A sequence of instantaneous power samples are calculated from IQ waveform samples by 

|IQ[n]|

2

P[n] = , (6.4) 2Z0

where Z0 = 50 Ω is the reference impedance of the sensor input. The time-series power statistics are computed for a 

1630 single-channel IQ capture by the following procedure. 

61 
DRAFT

0 1 2 3 4

Time (s)

100

95

90

85

Channel Power (dBm / 10 MHz)

Simulated Channel Power, t = 10 ms [SystemNoise]

Det: Mean, Pavg = -99.9 dBm / 10 MHz

Det: Max, Pmax = -87.0 dBm / 10 MHz

System Noise, Fn = 4.1 dB

Figure 6.8: Mean and max. detected power statistics over a 4 s interval for simulated system noise in a 10 MHz 

channel, with simulated channel noise fgure Fn = 4.1 dB. The predicted noise foor is shown as a black dashed line. 

• Scale IQ samples by the calibrated sensor gain per Equation 6.2. 

• Filter IQ samples using the IIR flter described by Equation 6.3 and Table 6.2. 

• Convert IQ samples (Volts) to instantaneous power samples (Watts) using Equation 6.4. 

• Reshape instantaneous power data into 400 contiguous blocks of 140,000 samples each, with each block of samples 

1635 representing power samples in contiguous 10 ms blocks of time. 

• For the RMS power trace: Take the mean of each block of samples, yielding 400 samples representing timesequential RMS signal power on 10 ms timeframes. 

• For the peak power trace: Take the maximum of each block of samples, yielding 400 samples representing timesequential peak signal power on 10 ms timeframes. 

1640 • Convert both results from Watts to dBm. 

Examples: 

Examples of these power statistics are shown in Figures 6.8, 6.9, and 6.10. Figure 6.8 shows the results for simulated 

system noise. In this fgure, the RMS power detector results accurately refect the simulated system’s expected noise 

foor. Figure 6.9 shows the results for simulated system noise and a simulated “Radar 1” signal, with its boresight 

1645 pulse occurring at t = 0.25 s. In this fgure, the simulated radar pulse is clearly visible as a spike in both traces; the 

RMS power trace shows a smaller spike, since the radar pulse time is smaller than the detector window of 10 ms. Note 

that the channel power average, Pavg = 99.9 dBm deviated by only 0.1 dBm from the value for simulated system 

noise without radar shown in Figure 6.8, while the instantaneous peak channel power Pmax deviates substantially. 

Figure 6.10 shows the results for real-world data, in the 3590-3600 MHz channel, recorded by the prototype sensor 

1650 deployed at Green Mountain Mesa in Boulder, Colorado. 

6.1.3.2 Power Spectral Density 

Description: The power spectral density quantifes the distribution of signal power with respect to frequency within 

a waveform capture. A set of power spectra are computed from the IQ waveform, each with frequency resolution 

∆f = 16 kHz. Peak and RMS statistical detectors are then applied to give complementary insights about short bursts 

1655 and average power, respectively. The frequency resolution of the computed spectra is chosen to provide insight into 

the frequency distribution of the power of cellular communication signals within their resource blocks, and to allow 

for seamless aggregation of adjacent channel results. Windowing is used to reduce the undesirable effects of spectral 

leakage and scalloping loss. 

62 
DRAFT

0 1 2 3 4

Time (s)

100

95

90

85

80

75

70

65

Channel Power (dBm / 10 MHz)

Simulated Channel Power, t = 10 ms [SysNoise_and_Radar1]

Det: Mean, Pavg = -99.9 dBm / 10 MHz

Det: Max, Pmax = -68.2 dBm / 10 MHz

System Noise, Fn = 4.1 dB

Figure 6.9: Mean and max. detected power statistics over a 4 s interval for simulated “Radar 1” and system noise in 

a 10 MHz channel, with simulated channel noise fgure Fn = 4.1 dB. The predicted noise foor is shown as a black 

dashed line. 

0 1 2 3 4

Time (s)

100

95

90

85

80

75

70

Channel Power (dBm / 10 MHz)

Channel Power, t = 10 ms, 3590-3600 MHz [CO Prototype 01-10-2023]

Det: Mean, Pavg = -92.9 dBm / 10 MHz

Det: Max, Pmax = -71.8 dBm / 10 MHz

System Noise, Fn = 4.0 dB

Figure 6.10: Mean and max. detected power statistics in the 3590-3600 MHz channel over a 4 s interval of a realworld signal recorded on January 10, 2023 by the prototype sensor at Green Mountain Mesa, Colorado. The system’s 

calibrated noise foor in the channel is shown as a black dashed line. 

Background: Representing RF signals as functions of frequency rather than time is a common practice in radio 

1660 science and engineering. Viewed this way, it is possible to easily detect important signal features and characteristics, 

such as dominant signal frequencies or the presence of frequency-dependent signal structures. The discrete Fourier 

transform (DFT) provides a method for sampling the discrete-time Fourier transform (DTFT), which is equivalent 

within the Nyquist bandwidth to the Fourier transform. The DFT calculates the sequence X[m] from an input IQ[n]

by: 

X[m] =

N

X−1

IQ[n] · exp − j2πnm

N



(6.5) 

n=0

1665 The corresponding sequence of frequency values F[m] is determined using the sampling rate Fs and center frequency 

fc pertaining to the input sequence IQ[n] by: 

63 
DRAFT 

Table 6.3: Coeffcients for the fat top window used in PSD statistics processing. 

A0 0.21557895 

A1 -0.41663158 

A2 0.277263158 

A3 -0.083578947 

A4 0.006947368 

mFs F[m] = + f0, 0 ≤ m ≤ N − 1 (6.6) N

The DFT is commonly implemented in practice using the fast Fourier transform (FFT) algorithm, which allows for the 

DFT to be computed much more effciently than by using Equation 6.5 directly (O(N log N) vs. O(N2)) [17]. 

One characteristic of the DFT is spectral leakage, which occurs due to the implicit assumption in the DFT that the 

1670 input signal is exactly periodic over the length of the input. For any frequency component of the input which is not an 

exact multiple of ∆f, this assumption fails. Since all recorded signals cannot be expected to be constrained in this way 

for any choice of ∆f, leakage is practically unavoidable in DFT-based processing for this measurement application. 

Windowing is therefore used to reduce the effects of leakage. Windows are periodic functions which go to zero at 

the frst and last sample of the DFT input, satisfying the periodicity assumption of the DFT. Many types of windows 

1675 exist, each with various advantages and disadvantages. The fat top window was chosen due to its good performance 

in reducing spectral leakage as well as its optimal performance in reducing scalloping error. The fat top window 

has worst-case scalloping error of 0.01 dB, while demonstrating maximal fatness in its main lobe [18]. The fat top 

window sequence is a 5th-order cosine function, defned by Equation 6.7 and Table 6.3. 

L

X−1   2πln

w[n] = Al · cos (6.7) 2N + 1 l=0

The application of the window inherently reduces the amplitude and energy of the signal to which it is applied. In 

1680 order to accurately measure the power spectral density at the calibration terminal, the window energy correction factor 

is calculated from the window by Equation 6.8. 

s N ECF = P (6.8) N−1

w[n]

2 n=0

Calculation: The power spectral density statistics are computed for a single-channel IQ capture by the following 

procedure. Note that the net effect of the various scaling factors applied is to remove the effects of windowing, 

normalize the FFT, and account for the equivalent noise bandwidth (ENBW) of the DFT bins. The results are the mean 

1685 and maximum power spectral densities, with sample amplitudes in dBm/Hz, referenced to the calibration terminal. 

• Scale IQ samples by the calibrated sensor gain per Equation 6.2. 

• Filter IQ samples using the IIR flter described by Equation 6.3 and Table 6.2. 

• Scale IQ samples for the amplitude loss due to windowing, by multiplying each sample by the window energy 

correction factor defned in Equation 6.8. 

1690 • Reshape IQ samples into 64,000 contiguous blocks of 875 samples each. 

• Apply the window by multiplying each block element-wise by the fat top window sequence defned by Equation 6.7 

64 
DRAFT

fc 4 fc 2 fc fc + 2 fc + 4

Frequency (MHz)

175

170

165

160

155

Power Density (dBm / Hz)

Simulated Single-Channel PSD, f = 16 kHz [SystemNoise]

Det: Mean, Pavg = -99.9 dBm / 10 MHz

Det: Maximum

System Noise, Fn = 4.1 dB

Figure 6.11: Mean and max. detected power spectral density for simulated system noise in a 10 MHz channel, with 

simulated channel noise fgure Fn = 4.1 dB. The predicted noise foor is shown as a black dashed line. 

and Table 6.3. 

• Compute the DFT of each block (Equation 6.5 with N = 875) using the FFT algorithm. 

• Convert the DFT result amplitudes (Volts) to power (Watts) using Equation 6.4. This step include scaling to account 

1695 for RF-to-baseband downconversion. 

• Scale power values to a power spectral density (W/Hz) by frst dividing by the sampling rate Fs, then dividing by 

the DFT length N. 

• Sort the PSD samples by their corresponding frequencies. 

• Apply statistical detectors bin-wise; that is, the i-th sample in a detector result is determined from the i-th samples 

1700 of all 64,000 PSD results. 

• For the mean trace Take the mean of all PSD results, bin-wise. 

• For the maximum trace Take the maximum from all PSD results, bin-wise. 

• Convert both PSD results from Watts/Hz to dBm/Hz. 

• Discard the frst and last 125 samples from both PSD results, which represent frequency ranges outside the measured 

1705 channel. 

Examples: 

Examples of these power statistics are shown in Figures 6.11, 6.12, and 6.13. The values Pavg are computed by 

summation of the mean PSD results, and are shown to agree with those values calculated from the time series results 

shown in Figures 6.8, 6.9, and 6.10 respectively, which were generated from the same data. Figure 6.11 shows the 

1710 results for simulated system noise. In this fgure, the mean PSD detector results accurately refect the simulated 

system’s expected noise foor. Figure 6.12 shows the results for simulated system noise and a simulated “Radar 1” 

signal centered on the simulated baseband. In this fgure, the main lobe and some sidelobes of the simulated radar 

signal appear as wide peaks in the maximum PSD result. The mean PSD does not indicate the presence of radar due to 

the very brief radar pulse time. Figure 6.13 shows the results for real-world data, for the entire CBRS band, recorded 

1715 by the prototype sensor deployed at Green Mountain Mesa in Boulder, Colorado. This fgure shows an example of 

multi-channel data aggregation: the data shown was computed from 15 separate, channelized IQ recordings. The 

choice of N = 875 allows for this aggregation with constant sample spacing, even across channel boundaries. 

65 
DRAFT

fc 4 fc 2 fc fc + 2 fc + 4

Frequency (MHz)

175

170

165

160

155

150

145

140

Power Density (dBm / Hz)

Simulated Single-Channel PSD, f = 16 kHz [SysNoise_and_Radar1]

Det: Mean, Pavg = -99.9 dBm / 10 MHz

Det: Maximum

System Noise, Fn = 4.1 dB

Figure 6.12: Mean and max. detected power spectral density for simulated “Radar 1” and system noise in a 10 MHz 

channel, with simulated channel noise fgure Fn = 4.1 dB. The predicted noise foor is shown as a black dashed line. 

3560 3580 3600 3620 3640 3660 3680 3700

Frequency (MHz)

175

170

165

160

155

150

145

140

135

130

125

Power Density (dBm / Hz)

PSD, f = 16 kHz, 3550-3700 MHz [CO Prototype 01-10-2023]

Detector: Mean

Detector: Maximum

System Noise

Figure 6.13: Mean and max. detected power spectral density for real-world signals in the CBRS band, with calibrationpredicted channel noise foors shown as black dashed lines. The data was recorded on January 10, 2023 by the 

prototype sensor at Green Mountain Mesa in Colorado. 

Periodic Frame Power 

Description: We developed the periodic frame power measurement for this project to help differentiate between 

transmissions from CBSDs and others, including pulsed radar, for forensic application Spectrum Access System (SAS) 

The underlying approach is to capture periodic channel occupancy characteristics that are fxed 

parameters of these different types of users. 

Background: Past monitoring campaigns support the idea that channel occupancy and power can be used to help 

classify between downlink and uplink. Downlink transmissions tend to exhibit high channel occupancy, and typically broadcast in almost every frame [19]. In contrast, uplink signaling, which often consumes battery power in 

mobile devices, is optimized to minimize transmission time; as a result, measurements of uplinks showed dramatically 

6.1.3.3 

1720 

activation events. 

1725 

lower occupancy rates, sometimes by factors as large as 106 [19]. These factors, together, lead to the hypothesis that 

aggregate power received by a SEA sensor strongest and least variable during downlink transmissions. 

Each of the most widely-discussed types of incumbent and entrant users in CBRS have fxed and clearly defned 

66 
1730

1735

1740

1745

1750

1755

1760

1765

1770

DRAFT 

periodic occupancy characteristics. 

• 5G cellular CBSDs operate time division duplex (TDD) networks with frame duration 10 ms. A base station subdivides the frame into a schedule of interleaved downlink (base station) and uplink (user equipment) time allocations. 

The schedule of allocations has time resolution equal to 1 symbol, which in the CBRS band may have duration 

1/56 ms, 1/28 ms, or 1/14 ms [20]. Cellular standards defne a large number of different such interleaved schedules that 

may be selected by the base station. These may remain fxed during normal operation of the base station. 

• 4G cellular CBSDs operate TDD frames with duration fxed at 10 ms by 3rd-generation partnership project (3GPP) 

standards [21]. A base station subdivides the schedule into a schedule of interleaved downlink and uplink time allocations. These allocations have time resolution of 1 ms. Only 7 different duplex scheduling patterns are supported, 

and remain fxed during normal operation of a base station. 

• IEEE 802.16 (WiMaX) CBSDs operate TDD networks. A network operates in a fxed frame duration, which must 

be one of {2, 2.5, 4, 5, 8, 10, 12.5, 20} ms [22]. Of these, equipment is only required to support 5 ms for WiMaX 

Forum certifcation, making this a common assumption for the actual frame duration in these networks. Recent 

standards also defne a superframe as a sequence of 5 ms frames, reinforcing 5 ms as the base periodicity constant. 

• “Radar 1” refers to one of the simulated pulsed radar systems that is used to test CBRS Environmental Sensing 

Capabilitys (ESCs) according to [23, Table 1]. The signal in these tests has pulse repetition interval close to 1 ms, 

and pulse width between 0.5 μs and 2.5 μs. 

In each of these cases, a fxed division period is defned that is further subdivided to determine transmission timing. 

When transmissions repeat occupancy patterns within these periods, we have an opportunity to simplify the short-time 

features of the channel power by characterizing their typical behavior. 

The key to performing power measurements of these characteristics is to defne a reference frame that is a common 

multiple of all division periods under study. For our reference implementation, we selected 10 ms (Table 6.1), since it 

is the least common multiple of 1 ms, 5 ms, and 10 ms. Pointedly, this means that useful structure will not be visible for 

transmissions with aperiodic occupancy characteristics, or those which are not close to integer divisible from 10 ms. 

Calculation: The calculation of periodic frame power begins with the time-series of instantaneous power samples Pn

calculated from equation (6.4), and proceeds as follows: 

1. Partition the time-series into M contiguous reference frames, following the reference frame duration (Table 6.1). 

For a waveform capture length of 4 s, this produces M = 400 frames per waveform. 

2. Partition each frame into N power bins. The time-duration of these bins should not exceed the shortest possible 

transmission occupancy event. The reference duration of the power bins corresponds with the shortest possible 

duration of a single symbol defned for CBRS in 5G cellular standards: including the cyclic prefx, this duration is 

approximately 17.9 μs. This results in N = 560 power bins per reference frame. 

3. Apply a power detector to evaluate channel power in each power bin. The reference implementation evaluates both 

peak and RMS power detectors, which are reported as separate measurement traces. 

4. Compute statistics across nth power bin of each frame. The resulting frame power measurement trace has M samples, each of which has been computed across the N frames. The reference implementation records {min, mean, max}

for each of the peak and RMS power detectors. 

The reference frame duration should also be an integer multiple of the waveform sampling period. 

Examples: The interpretation and usage of PFP is most easily conveyed by concrete example. To this end, we start 

with some simple simulation data, and then real feld data that were observed with prototype sensors. 

A simulation of the simple case of a quiet channel is illustrated by Figure 6.14, composed entirely of noise power 

67 
DRAFT

0 2 4 6 8 10

Cyclic Time (ms)

106

101

96

91

86

Channel Power (dBm / 10 MHz)

Simulated Single-Channel PFP, mod t = 10 ms [SystemNoise]

RMS Power, Pavg = -99.9 dBm / 10 MHz

Peak Power, Pmax = -87.0 dBm / 10 MHz

System Noise, Fn = 4.1 dB

Figure 6.14: Peak and RMS-detected PFP results for simulated system noise in a 10 MHz channel, with simulated 

channel noise fgure Fn = 4.1 dB. The predicted noise foor is shown as a black dashed line. 

0 2 4 6 8 10

Cyclic Time (ms)

106

101

96

91

86

81

76

71

66

Channel Power (dBm / 10 MHz)

Simulated Single-Channel PFP, mod t = 10 ms [SysNoise_and_Radar1]

RMS Power, Pavg = -99.9 dBm / 10 MHz

Peak Power, Pmax = -68.2 dBm / 10 MHz

System Noise, Fn = 4.1 dB

Figure 6.15: Peak- and RMS-detected PFP measurements for simulated “Radar 1” and system noise in a 10 MHz 

channel, with simulated channel noise fgure Fn = 4.1 dB. The predicted noise foor is shown as a black dashed line. 

samples. With an assumed noise fgure of 4.1 dB, most of this power produced inside the sensor receiver. The solid 

lines represent the mean of the RMS power detector (blue) and peak power detector (red) readings; the average at 

each point is evaluated across the M = 400 data points evaluated at the same “cyclic time,” which is the time 

elapsed into each 10-ms block. The shaded envelopes around each trace bound the extrema across the same M data 

points. Because the expected value of the thermal noise power in the receiver is constant, there is no time structure 

here except for random noise variability. Notice that the extrema for the RMS power detector are particularly close 

together, because the averaging detector has reduced the standard error of the noise power measurement. 

Adding simulated “Radar 1” activity to the thermal noise produces the PFP illustrated by Figure 6.15. The pulse 

repetition interval of the waveform, which is very close to 1 ms, repeats almost exactly 10 times per frame. As a result, 

peaks occur close to the same cyclic time elapsed in each frame, at least relative detector resolution of 17.9 ms. The 

periodic characteristic is then very clear by inspection of the plot. Meanwhile, the RMS detector still shows the noise 

foor at the same power level as without the pulses, unchanged from Figure 6.14. 

1775 

1780 

In order to illustrate CBSD activity, we move to examples of feld data, since it is the duplexing dynamics cellular 

and WiMaX systems are proprietary and diffcult to simulate. One such example is shown by Figure 6.16, taken from 

1785 the prototype sensor in Colorado. In contrast with the clear pulse train in Figure 6.15, the periodic response here is 

68 
DRAFT

0 2 4 6 8 10

Cyclic Time (ms)

106

101

96

91

86

81

76

71

Channel Power (dBm / 10 MHz)

PFP, mod t = 10 ms, 3590-3600 MHz [CO Prototype 01-10-2023]

RMS Power, Pavg = -92.9 dBm / 10 MHz

Peak Power, Pmax = -71.8 dBm / 10 MHz

System Noise, Fn = 4.0 dB

Figure 6.16: Peak and RMS-detected PFP results for real-world signals in the 3590 MHz – 3600 MHz channel, with 

the calibration-predicted channel noise foor shown as a black dashed line. The capture was recorded on January 10, 

2023 by the prototype sensor at Green Mountain Mesa in Colorado. 

divided into three regions: a steady broadcast at the highest power level, a more variable period that includes a pedestal 

about 15 dB lower, and a quiet period consistent with the system noise power. Further study and comparison against 

well-characterized CBSD traffc may in the future permit more specifc conclusions to be drawn about the nature of 

the data networks based on these plots. However, the distinction against pulsed radar is clear. 

1790 Uses: As a culminating application of PFP is as a forensic tool to investigate CBRS DPA potential activation events. A 

real-world example is illustrated by Figure 6.17, for an event observed by the prototype sensor deployed at the Norfolk 

International Terminal (NIT) site in Virginia. 

In order to start with a view across a longer time period, simple statistics were on the PFP trace in each capture 

for 16 minutes in Figure 6.17(a): a “capture median” (the median of average RMS detector readings), and “capture 

1795 max” (maximum of the peak detector readings). Just after 7 min time elapsed, a capture maximum power reading has 

increased by about 10 dB, without signifcant change in the capture median. This suggests that a pre-existing signal 

has continued while short bursts have become present. Then, near 10 min elapsed, the capture median falls to the 

receive system noise level: for most samples in these captures, there is no detectable activity in the channel. 

Figure 6.17(b) details PFP captures at the 3 key points marked along sub-fgure (a). First, at 5.7 min., we see the 

1800 envelope of ongoing TDD CBSD activity in the channel. By 8.6 min., a pulse train has emerged that is superimposed 

on the pre-existing channel activity; here, the variability in the RMS trace has decreased signifcantly, indicating a 

change in CBSD occupancy behavior, though the duration of its “off” time remains unchanged. By 12.5 min., the 

CBSD activity has stopped, and the pulse behavior has changed. The pulse repetition interval, at approximately 1 ms, 

matches the pulse repetition interval “Radar 1,” though this is not by itself a conclusive indication of that this specifc 

1805 type of incumbent user is present in the channel. 

When combined with other data streams, a PFP analysis like the above represents a powerful tool to add confdence to 

conclusions about the CBRS environment. For example, comparisons against logs of SAS channel grants lead CBSDs 

to respond as expected in terms of physical channel radio channel occupancy. In test cases where “ground truth” data 

is available that can confrm the presence of incumbents in the sensor’s DPA, the PFP can be used to help determine 

1810 whether power detected by the SEA sensor originates from incumbent or CBSD transmissions. 

69 
DRAFT

0 2 4 6 8 10 12 14

Time elapsed (minutes)

100

80

60

40

5.7 min8.6 min

12.5 min

(a) Long-Time Trend Across Captures

RMS detect (capture median) Peak detect (capture max)

100

80

60

40

5.7 min: CBRS before pulses

(b) Capture Details

RMS detect

Peak detect

Capture median

Capture max

100

80

60

40

8.6 min: Pulses appear

0 2 4 6 8 10

100

80

60

40

12.5 min: Pulses continue after CBSD activity halts

Channel power (dBm)

Cyclic time elapsed (ms)

PFP at 3595 MHz [Field Data, NIT Prototype, Nov 2022]

Figure 6.17: Measurements of PFP during a possible Dynamic Protection Area (DPA) activation event. First, time 

series of capture-by-capture statistics (a) exhibits an abrupt drop in median RMS power. On further investigation, PFP 

traces in (b) detail CBSD activity before the event (“Capture 1”), followed by the appearance of pulses with 1 ms pulse 

repetition interval (“Capture 2”), and later the same pulse pattern but without CBSDs. The frst capture with pulse 

activity occurred approximately 3 min before the frst capture without CBRS. 

Amplitude Probability Distribution 

Description: An APD is the complementary cumulative distribution function (CCDF) of the instantaneous channel 

power, evaluated empirically. It is evaluated across the full duration of each channel waveform capture. Physically, 

it is equivalent to the percentage of time in which the capture exceeds a specifed power threshold. Plots of the APD 

provide qualitative insights to the duty cycle of a measured signal. Rare occurrences of high amplitude samples across 

a full capture may indicate the presence of a strong, briefy pulsed signal, such as pulsed radar. Quantitative analysis 

may also leverage the APD to estimate occupancy characteristics in the channel. For example, assuming bursted noise6.1.3.4 

1815 

like transmissions in CBRS wireless data networks, the transmission time-occupancy rate may be estimated based on 

the deviation between the APD and an assumed underlying distribution, such as idealized Gaussian noise. 

1820 Background: The original application for the APD was for the characterization of noise originating in the atmosphere 

70 
DRAFT 

 



 

1825 

1830 

1835 

1840 

1845 

1850 

at low frequencies [24]. Because the APD makes no assumption about time structure of the electromagnetic environment, it can be computed independent of wireless networking parameters, unlike periodic frame power (PFP). In 

conventional graphical representation, the vertical axis is power transformed to decibel units; the horizontal axis is the 

“percent exceeding ordinate” with a transformation to Q − Q probability space such that idealized thermal noise can 

be represented as a straight line. 

We calculate a discretized APD following Achatz et al [25]. The continuous distribution is expressed in terms of the 

random variable X , a function which assigns a real number X (ν) to each element ν of a sample space. The APD is 

expressed in terms of the probability operator P and signal amplitude a by Equation 6.9. 

F(a) = P(X (ν) > a) (6.9) 

The discretized APD F[n] is obtained from the input of N complex baseband samples, IQ[n]. First, the amplitudes 

are computed from the IQ samples, as in Equation 6.10, and sorted in order from smallest to largest: 

�  a[n] = sort IQ[n] . (6.10) 

Finally, the probabilities are defned based on these sorted amplitudes by Equation 6.11. 

�  n P[n] = P X > a[n] = 1 − (6.11) N

This method of obtaining a discretized APD is simple and accurate, yet not practical for continuous real-time sensing. 

The operation to sort all samples as per Equation 6.10 is computationally costly, which slows down the sweep time 

across the band. Additionally, the APD output vector has, for both axes, the same dimension as the input vector. 

This situation is (1) far too large to stream back over the available backhaul solution, at 56 megasamples per 10 MHz 

channel, and (2) far more detailed along the probability axis than is useful. In a successful effort to optimize IQ 

post-processing time and output data size, an optimized method was implemented which is based on the defnitions of 

the APD estimate above. In the optimized routine, amplitudes are replaced with amplitude bins, defned based on a 

parameterized power bin size. The bin edges are defned to span from the minimum amplitude value up to (potentially 

including but not exceeding) the maximum amplitude value, with uniform spacing by the determined bin size. The 

output size of the optimized APD therefore depends both on the choice of bin size parameter as well as the dynamic 

range of the recorded signal, and varies from one capture to the next. Then, the indices are found in the sequence 

of bin edges at which the unsorted amplitude samples could be inserted while maintaining the sorted ordering. The 

numbers of occurrences of each index are then counted to give the distribution of amplitude values within in each bin. 

Finally, these occurrences are cumulatively summed and subtracted from the input length N, before being normalized. 

This method removes the requirement for a large-input sort operation, and solves both previously mentioned issues. 

The result is a deliberate trade-off, where resolution is reduced in favor of faster computation and less output data per 

capture. 

Calculation: The APD is computed for each single-channel waveform capture as follows: 

1. Scale IQ samples by the calibrated sensor gain per Equation 6.2. 

2. Filter IQ samples using the IIR flter described by Equation 6.3 and Table 6.2. 

3. Calculate IQ sample amplitudes by taking the absolute value of each sample, IQ[n] . 

4. If any amplitudes are zero-valued, replace them with NaN to avoid foating point arithmetic errors. 

71 
DRAFT

10 1 1 10 25 50 75 90 95 99 99.9

Percent Exceeding Ordinate

130

120

110

100

90

Channel Power (dBm / 10MHz)

Simulated Single-Channel APD, N = 56 × 106 [SystemNoise]

Pmax = -86.1 dBm / 10 MHz

Noise Floor, Fn = 4.1 dB

Figure 6.18: Downsampled APD result for simulated system noise in a 10 MHz channel, with simulated channel noise 

= 4.1 dB. The predicted APD of the system noise is shown as a black dashed line. 

10 1 1 10 25 50 75 90 95 99 99.9

Percent Exceeding Ordinate

130

120

110

100

90

80

70

Channel Power (dBm / 10MHz)

Simulated Single-Channel APD, N = 56 × 106 [SysNoise_and_Radar1]

Pmax = -68.0 dBm / 10 MHz

Noise Floor, Fn = 4.1 dB

Figure 6.19: Downsampled APD result for simulated system noise and “Radar 1” in a 10 MHz channel, with simulated 

= 4.1 dB. The predicted APD of the system noise is shown as a black dashed line. 

5. Generate the sequence of amplitude bin edge values, beginning with the smallest amplitude sample and increasing 

in uniform steps of half the parameterized power bin size, up to but not exceeding the maximum amplitude value. 

6. Using a binary search, locate the indices in the sequence of amplitude bin edges at which the unsorted amplitudes 

could be inserted while maintaining the sorted bin ordering. 

7. Count the number of occurrences of each located index value, representative of the number of amplitude samples 

8. Cumulatively sum the sequence of these counts, and subtract each value from the number of input IQ samples N. 

9. Normalize the resulting sequence by dividing each value by the number of input IQ samples N. The results are 

CCDF probabilities, within the interval [0, 1]. 

fgure Fn

channel noise fgure Fn

1855 

contained in each bin. 

1860 

10. Scale the resulting amplitude bin values from dBV amplitudes to power samples in dBm, accounting for system 

impedance and the power-scaling factor of 2 associated with the down-conversion of the RF signal to baseband. 

1865 Examples: 

72 
DRAFT

10 1 1 10 25 50 75 90 95 99 99.9

Percent Exceeding Ordinate

130

120

110

100

90

80

70

Channel Power (dBm / 10MHz)

APD, N = 56 × 106, 3590-3600 MHz [CO Prototype 01-10-2023]

Pmax = -71.1 dBm / 10 MHz

Noise Floor, Fn = 4.0 dB

Figure 6.20: Downsampled APD result for real-world signals in the 3590-3600 MHz channel, with the predicted APD 

of the system noise shown as a black dashed line. The data was recorded on January 10, 2023 by the prototype sensor 

at Green Mountain Mesa in Colorado. 

Metadata are recorded alongside each edge data sweep in a JSON-formatted text fle based on SigMF [15]. The 

metadata provide the context needed to parse and interpret the raw data, including summary statistics and diagnostic 

sensor information. Metadata are categorized into global metadata, channelized metadata, and sensor diagnostics. 

Global metadata felds contain information pertaining to the entire edge data acquisition, rather than to only a single 

channel measurement. These are listed in the example JSON fle excerpt in Figure 6.21. Not shown in the fgure, 

the global metadata also includes RMS and peak channel power summary statistics, selected details of processing 

parameters and data collection, and the data component lengths and indices for parsing the binary data fle. 

Channel metadata felds contain information pertaining to the individual channel measurement. These felds include 

sensor overload fags, IQ recording timestamps, signal analyzer settings, channel calibration data, and sample index 

values for parsing the attached data fle. The overload fag is triggered on a per-channel basis by the signal analyzer’s 

report of an analog-to-digital converter (ADC) overrange event; see section 4.4 for more details on the conditions that 

cause over-range. The sample index information is required for aggregation and analysis of the data payloads, since 

all data is stored in a contiguous binary format. 

Diagnostic metadata felds contain information pertaining to the sensor status and operation. These include computer 

load information and readouts from temperature and humidity probes in various parts of the sensor. 

Data Size and Structure 

The digital samples representing the results of the IQ processing described above are saved as half-precision (16-bit) 

foating point binary values. The sample array is converted to binary and compressed using lossless Lempel-ZivMarkov chain algorithm (LZMA) compression. Using this method, a typical compression ratio of approximately 

2.4:1 is achieved, yielding a data fle of approximately 70 kB from each measurement of the entire CBRS band. The 

corresponding data (compressed binary) and metadata (text JSON) fles are packaged into a single tar archive. After 

6.1.4 Metadata 

1870 

1875 

1880 

6.1.5 

1885 

this fle is generated, it is pushed back to a centralized NASCTN data repository over a private LTE network for 

further cataloging, post-processing, and aggregate analysis. If any error occurs during this fle transfer, the data is 

1890 saved locally to the sensor fash storage, where it can be later manually retrieved. 

73 
DRAFT 

"global": {

"core:version": "v0.0.2",

"core:extensions": {

"ntia-algorithm": "v1.0.0",

"ntia-core": "v1.0.0",

"ntia-environment": "v1.0.0",

"ntia-location": "v1.0.0",

"ntia-scos": "v1.0.0",

"ntia-sensor": "v1.0.0",

"ntia-nasctn-sea": "v0.3"

},

"core:datatype": "rf16",

"core:sample_rate": 14000000.0,

"core:num_channels": 15,

"ntia-scos:task": 1,

"ntia-scos:schedule": {

"name": "example_data_product",

"start": "2023-01-04T23:47:59.000Z",

"stop": null,

"interval": 90,

"priority": 10,

"id": "example_data_product"

},

"ntia-sensor:calibration_datetime": "2023-01-04T23:49:00.311Z",

"calibration_temperature_degC": 0.60009765625,

...

}

Figure 6.21: Example of global metadata felds contained in JSON metadata fles 

6.2 Aggregation 

The sensor data streams recorded from each sensor need to be aggregated into a centralized location in order analyze 

the results across time and location, and to disseminate the data. The detailed approach to this will be developed by 

NASCTN concurrently with data collection in the feld. 

1895 Data aggregation will initially consist of a local database hosted on a server accessible for downloading raw data 

products or via a web interface to be developed at a later time. Periodically, new records received from the streams of 

sensor data will be parsed and ingested into a database. In this form, the data is available for rapid querying to support 

subsequent post-processing. In this form, batch data acquisitions can be performed by NASCTN or external staff as 

needed on data that aggregated across time and sensor site. Key quantities derived from the sensor data can also be 

1900 added to the database in order to allow for rapid retrieval of pre-calculated measurements. 

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Chapter 7 

Calibration, Verifcation, Acceptance Tests 

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1905 

1910 

1915 

7.1 Amplitude Calibration 

The sensor system is intended to measure power in the Citizens Broadband Radio Service (CBRS) frequency allocation (3550-3700 MHz). To ensure the power arriving at the input port is accurately reported by the sensor, 

an amplitude calibration procedure has been designed. The amplitude calibration is a combination of an onboard 

radiometer-characterized noise diode and a programmable noise probe. The onboard noise diode allows the system to 

update the system power calibration and provides diagnostic information about the gain and noise fgure of the system 

as deployed. The programmable noise probe functions as a variable noise signal generator with known noise power 

levels. The programmable noise probe is used to calibrate the sensor at the input port of the preselector without the 

need of dissembling the sensor. In Figure 7.1, a simplifed block diagram of the sensor is shown. The antenna port 

is labeled as reference plane 1, the output of the on-board calibration noise diode is labeled as reference plane 2. To 

accurately measure power arriving at the calibration plane labeled C from the plane marked 1, we need to calculate 

the transducer gain ratio G2→C /G1p→C . T T

7.1.1 Calibration of the On-board Calibration Noise Diode 

Both the programmable noise probe and the on-board calibration noise diode are calibrated with noise temperatures, 

Tp and TND respectively. Note that the noise diode (ND) has two known states; hot (voltage source on) and cold 

(ambient, voltage source off). The power measured by the signal analyzer is given by the equations [26] 

h i 1p→C 1p→C Pp = kBB G + (MC−p − G )Ta G (7.1a) T Tp + MC−pTs T

 G2→C PNDh = kBB TNDh + MC−NDhTs + (MC−NDh − G2→C )Ta G (7.1b) T T

PNDc = kBBMC−NDc (Ts + Ta) G (7.1c) 

Figure 7.1: The calibration block diagram, identifying the reference plane of antenna input (1), the reference plane of 

the calibration diode (2) and the calibration plane (C). The probe is inserted into the antenna port with refection Γp, 

and then is replaced by the antenna in the feld with refection Γa, the refection of the sensor input is Γs

Here kB is the Boltzmann constant. B is the bandwidth set by the signal analyzer or the post-processing digital signal 

processing (DSP) (usually the frequency bin size). Ts is the equivalent noise temperature of the sensor at plane ‘C’. G

is the deliverable gain from ‘C’ to the signal analyzer. The inclusion of the isolator (the green component in Figure 7.1) 

allows us to approximate the mismatch factors at plane ‘C’ for dissimilar input terminations MC−p ≈ MC−NDh ≈

MC−NDc ≡ M. As a result, (7.1) can be approximated as 

Pp = kBBG1p→C (Tp − Ta)G + kBBM(Ts + Ta)G (7.2a) T

PNDh = kBBGT

2→C (TNDh − Ta)G + kBBM(Ts + Ta)G (7.2b) 

76 
DRAFT 

PNDc = kBBM (Ts + Ta) G (7.2c) 

T The three linear equations in (7.2) provide a solution of the transducer gain ratio G2→C

1p→C . We label it as β. GT

(PNDh − PNDc)(Tp − Ta) β = (7.3) (Pp − PNDc)(TNDh − Ta)

In the feld, the parameter of interest is Tx, the feld radiation temperature measured at the antenna port. When the 

antenna is connected to the sensor, the transducer gain from ‘1’ to ‘C’ is G1a→C . Similar to (7.2a), the power measured T

at signal analyzer in the feld is 

Px = kBBG1a→C (Tx − Ta)G + kBBM(Ts + Ta)G (7.4) T

In combination with the measurements of the programmable noise probe in the feld, we obtain: 

G2→C

T Px − PNDc Tx = (TNDh − Ta) + Ta (7.5a) G1a→C

T PNDh − PNDc

1p→C GT Px − PNDc = β (TNDh − Ta) + Ta (7.5b) G1a→C

T PNDh − PNDc

Note that the Ta’s in (7.3) and the Ta’s in (7.5) in practice may deviate substantially. Ta’s in (7.3) are measured in the 

laboratory. β is determined in the lab experiments prior to the sensor deployment. Ta’s in (7.5) are frequently updated 

by temperature monitoring devices in the feld. In addition, PNDh and PNDc in (7.5) are calibrations performed in the 

feld. 

1p→C

T 1920 We determine the ratio G

G

1a→C : T

From network analysis, the transducer gain relates to the refection coeffcients of the input termination and the receiver. We can express the ratio by [27] 

1p→C G (1 − |Γp|

2)/|1 − ΓpΓs|2

T = (7.6) G1a→C (1 − |Γa|

2)/|1 − ΓaΓs|2

T

In virtue of (7.6), we represent (7.5) by 

(1 − |Γp|

2)|1 − ΓaΓs|2 (Px − PNDc) Tx = β |

2 (TNDh − Ta) + Ta (7.7) (1 − |Γa|2)|1 − ΓpΓs (PNDh − PNDc)

7.1.2 Discussion 

This treatment demonstrates that the power reading on the sensor is linearly related to the impinging signal temperature. To facilitate understanding, we may combine and regroup Equation 7.7, as a simple linear equation 7.8. We 

(1−|Γp|

2)|1−ΓaΓs|2 (TNDh−Ta) begin by defning the term α equal to the α = β after algebraic manipulation we are (1−|Γa|2)|1−ΓpΓs|2 (PNDh−PNDc)

left with 

Tx Ta Px = + PNDc − (7.8) βα βα

or more succinctly 

Px = ATx + B (7.9) 

77 
DRAFT Pon y = Poff 

(7.11) 

ENR NF = y − 1 (7.12) 

Pon Geff = kBTB(ENR + NF) (7.13) 

1925 

1930 

1935 

1940 

1945 

1950 

Where A is related to the effective gain and B is a gain scaled constant that represents the ambient noise temperature 

and the noise temperature of the sensor. The calibration procedure quantifes A and B at a given moment in time at 

a specifc ambient temperature. It is assumed that both of these coeffcients can change with time as evidenced by 

Figure 4.15 (due to ageing and fnite system stability) and with temperature (changes to system gain). The onboard 

calibration noise diode integrated into the sensor allows us to quantify these changes. The more common linear form 

is 

Px = Geff∆F kBTx + Geff∆F kB(Tambient + Tsensor) (7.10) 

Where Px is the reading of the power sensor, Geff is the effective gain in linear units, ∆F is the bandwidth, kB is 

Boltzmann’s constant, Tx is the signal noise temperature, Tambient is the noise temperature at the ambient physical 

temperature, and Tsensor is the noise temperature added by the sensor. For simplicity an estimator of Geff was used 

instead of the full form derived from 7.7. The estimator ignores the difference in impedance matching from the 

programmable noise diode attached to the input of the sensor and the antenna. In addition, the estimator of Geff is 

found by using the on-board calibration noise diode installed in the preselector of the sensor. This path is assumed to 

have a small but fxed difference to the antenna path. For the purposes of this test plan, data collected by the sensor 

has been adjusted using this estimator and not the full form. The data presented in this test plan thus has higher 

uncertainties in power than expected in the long term. Production level Sharing Ecosystem Assessment (SEA) sensor 

will follow the full form calibration procedure, adding to expectations of measurement rigor, and inclusion of terms 

ignored by the simple estimator. 

7.1.2.1 Calibration of Sensor Data 

Periodically, the sensor performs a self calibration to estimate Geff, the sensor noise fgure, and to update PNDh and 

Pa. The self calibration procedure frst changes the position of the switch in Figure 7.1 from the antenna input port to 

the onboard noise diode path. Then the noise diode is alternatively energized and deactivated. A recording of in-phase 

and quadrature (IQ) samples is made at 14 mega samples per second (Msps) for 1 s in each state, then the process 

is repeated in each 10MHz channel. Each sequence of IQ samples is then fltered using a 10 MHz band-pass flter 

with a known equivalent noise bandwidth (ENBW). The implementation of this flter is defned by Equation 6.3 and 

Table 6.2, and further described in Section 6.1.2.3. Then, power is calculated from each recording using the method 

defned in Equation 6.4 and outlined in subsubsection 6.1.3.1. The ratio of these two powers are then used to calculate 

the nominal gain and noise fgure of the sensor using the Y-Factor method [28] (without the correction for β), as shown 

in Equations 7.11 through 7.13. These calibration values are stored and reported with each data product measurement. 

The effective gain is used to scale the recorded IQ transforming them into physical units. 

Here, Pon and Poff are the recorded root mean square (RMS) powers in linear units when the noise diode is energized 

and deactivated, respectively. The noise diode’s excess noise ratio (ENR) and noise fgure (NF) are computed in these 

equations in linear units. The noise factor is the noise fgure in linear units. In Equation 7.13, kB is the Boltzmann 

constant, T is the temperature of the noise diode, and B is the ENBW of the digitally-implemented band-pass flter. 

Once the calibration is estimated subsequent IQ streams recorded by the spectrum analyzer are scaled by Equation 6.2, 

using the values calculated above. The last calibration performed is applied until another is executed, for currently 

deployed sensors this is on an hourly basis. The NASCTN team will monitor for the calibration stability over time to 

78 
DRAFT 

further adjust the calibration cadence as needed. 

79 
DRAFT 

7.2 Measurement Uncertainty 

1955 There are two key uncertainties that we consider for the sensor system. The frst is the uncertainty in the reported 

power, and the second is the uncertainty of the noise fgure estimate. The uncertainty in these values will be refned 

in future versions of the sensor deployment. The calibration strategy outlined in 7.1, is designed to correct for variations of the reported power for frequency differences, changes due to temperature and the overall path loss difference 

between the onboard noise diode and the antenna path. Future sensor deployments will have uncertainties calculated 

1960 using the microwave uncertainty framework (MUF) [29], allowing for the uncertainties in Γp, Γa, and Γs to be accounted for (i.e. the term displayed in 7.2). For the currently deployed prototype sensors an empirical assessment 

follows. 

7.2.1 Reported Power Uncertainty 

The uncertainty in reported power is a combination of the uncertainty in the scalar correction of the IQ (see equation 

6.2), variability of the sensor in reading a known, fxed power, the difference in mismatch between deployments 

and any time or temperature variations that are not accounted for by the gain estimator. The sensor measures a 

known power source (noise diode) to estimate the actual power at the input port for a given reading of the spectrum 

analyzer. For the three deployed units the on-board calibration noise diode is precisely calibrated in the National 

Institute of Standards and Technology (NIST) radiometer. The uncertainty in the RMS power emitted from these 

diodes is extremely small, with ENR uncertainties of approximately 0.05 dB. For other sensor realizations the ENR 

was derived from the manufacturers measurements with an uncertainty of approximately 0.2 dB. The total sensor 

power uncertainty is always limited by the uncertainty of the noise diode power. The manufacturer specifcation for 

temperature variations of ENR is 0.01 dB

◦ , and variations in the gain estimator greater than this are assumed to be C

real changes in system gain and not a change in power emitted from the noise diode. To estimate the variation due to 

mismatch between the antenna when compared with the variable noise source the term 

(1 − |Γp|

2)|1 − ΓaΓs|2

(1 − |Γa|

2)|1 − ΓpΓs|2

is calculated using multiple measurements of the antenna in different laboratory locations. These locations were 

1965 chosen at random to refect an potential installation in an uncontrolled environment. In Figure 7.2, the result of this 

calculation can be seen. This ratio varies from 0.98 to 1.07 indicating a nominal difference of 0.03 from unity. 

80 
DRAFT

3560 3580 3600 3620 3640 3660 3680 3700

Frequency (MHz)

0.98

1.00

1.02

1.04

1.06

1.08

(1 | p|

2)(|1 a s|

2

)

(1 | a|

2)(|1 p s|

2

)

Antenna location: 1

Antenna location: 2

Antenna location: 3

Antenna location: 4

Figure 7.2: Comparison of Antenna Mismatches. The calibrated noise temperature reading requires adjusting for the 

antenna γa and that of the probe γp, every installation potential has a slightly different antenna mismatch. To assess 

the impact of this difference an antenna was measured in four locations. 

The temperature variation of the gain can be assessed from feld data from the sensor deployed at Hampton University. 

Data from the time period 10/06/2022 - 12/26/2022 is shown in Figure 7.3 for a single 10 MHz channel, identifed as 

channel 8 with the center frequency of 3625 MHz. It can be seen that the gain in logarithmic units is linearly dependent 

on the temperature, a ft of this dependence displays a slope of 0.06 dB

◦C 1970 . When the gain values are corrected for this 

dependence, the residual gain shows a random distribution that we approximate as a Gaussian distribution. The 

residual gain after correction is shown in Figure 7.4. To better represent the total of gain variations for the sensor that 

are not corrected for, this process was repeated for all 15 10 MHz channels and aggregated. In Figure 7.5, the results 

of this process can be seen. The residuals for 597540 measurements shows a standard deviation of 0.15 dB, which 

1975 leads to an estimate of the expanded uncertainty due to variability of the sensor to measure a known, fxed power and

any time and temperature variations unaccounted for as ±0.30 dB. 

81 
DRAFT

5 10 15 20 25 30

Temperature ( C)

30.0

30.5

31.0

31.5

32.0

Geff (dB)

Channel 8

Fit m = -0.057 dB/ C,

b= 32.223 dB

Figure 7.3: Gain (logarithmic units) versus temperature of channel 8 (center f = 3625 MHz) shows a clear linear trend, 

this is repeated for all channels 

82 
DRAFT

0.3 0.2 0.1 0.0 0.1 0.2 0.3

Residual Gain (dB)

0

1000

2000

3000

4000

5000

6000

7000

8000

Number of Occurences

Figure 7.4: Residual Gain After Detrending of Channel 8, Hampton University Sensor. 

83 
DRAFT

0.3 0.2 0.1 0.0 0.1 0.2 0.3

Residual Gain (dB)

0

10000

20000

30000

40000

50000

Number of Occurences

= 0.147

N=597540

Figure 7.5: Residual Gain after Detrending Temperature Dependence of All Channels, Hampton University Sensor. 

Histogram of the residual gain after detrending temperature dependence. 

84 
DRAFT 

7.2.2 Noise Figure Estimate Uncertainty 

The noise fgure estimate quantifes the amount of random noise added to the signal by the total sensor system, or 

its decrease in signal to noise ratio (SNR). This value represents the noise foor of the system, and is therefore a 

1980 valuable metric of performance. The sensor system is designed to be an incoherent (not time synchronized) power 

detector. The noise fgure is estimated by equation 7.12. From this equation, the uncertainty in the onboard noise 

diode’s ENR (0.05 dB) and the uncertainty in the power readings from the sensor when the noise diode is energized 

and de-energized play a major role in the uncertainty of the noise fgure. Any passive loss in the sensor between the 

antenna and the frst low noise amplifer (LNA) will increase the noise fgure in portion to the loss. This difference 

1985 can be seen when comparing 4.32 and 7.6, a 1-2 dB passive loss between the on-board calibration noise diode path 

and the path leading to the antenna increases the measured noise fgure in 4.32 and explains the smaller discrepancy 

between 4.32 and 4.12. We assume that this difference is fxed and the variation of the noise fgure with time and 

temperature is accurately refected by the measurement of the on-board calibration noise diode. In Figure 7.6 we show 

the variation of the estimated noise fgure (dB) versus temperature. Similar to Geff the noise fgure shows a linear 

trend with temperature when plotted in logarithmic units, with a slope of 0.013 dB

◦C 1990 and an intercept of 3.950 for 

channel 8. In Figure, 7.7, the histogram of the detrended noise fgures can be seen. The other channels show similar 

slopes as channel 8. After detrending all of the channels for their respective slope and intercept, and aggregating the 

results, a combined standard deviation of 0.02 dB is observed (see Figure 7.8). 

5 10 15 20 25 30

Temperature ( C)

3.9

4.0

4.1

4.2

4.3

4.4

Noise Figure (dB)

Channel 8

Fit m = 0.013 dB/ C,

b= 3.950 dB

Figure 7.6: Temperature Dependence of Noise Figure channel 8, Hampton University Sensor. 

85 
DRAFT

0.04 0.02 0.00 0.02 0.04

Residual Noise Figure (dB)

0

2000

4000

6000

8000

10000

Number of Occurences

Figure 7.7: Residual Noise Figure after Detrending Temperature Dependence channel 8, Hampton University Sensor. 

86 
DRAFT

0.06 0.04 0.02 0.00 0.02 0.04 0.06

Residual Noise Figure (dB)

0

20000

40000

60000

80000

100000

120000

Number of Occurences

= 0.020

N=597540

Figure 7.8: Residual Noise Figure after Detrending Temperature Dependence for All Channels, Hampton University 

Sensor. This histogram of the residual noise fgure after detrending temperature dependence shows a standard deviation 

of 0.02 dB 

87 
DRAFT 

7.3 Sensor Verifcation and Acceptance Tests 

1995 Once the sensor was constructed and identifed as operational, a series of tests for acceptance were administered. 

These tests were chosen to be system level verifcation and not necessarily component level tests. The verifcation was 

three-fold: 

1. RF characterization 

2. Software verifcation 

2000 3. System Power and communication verifcation 

Only once these three sections were completed, were the sensors considered fully deployed at the feld locations. 

7.3.1 RF Characterization 

For RF characterization, a calibration probe, consisting of an amplifed noise diode with a programmable attenuator 

was devised. This essentially operated as a noise diode with a settable ENR. A bench top sensor system used for initial 

2005 prototyping was used to determine the attenuation setting versus ENR for the calibration probe. 

Once the probe was calibrated, the completed sensors were measured with the calibration probe with a fxed ENR/attenuation 

from the antenna terminal in contrast to the on-board noise diode. This was done prior to deployment (see Figure 7.11) 

and at several stages during installation to ensure nothing was damaged during transit or assembly. 

Finally, the S11 of the sensor and installation cable was taken before deployment. Then, once installed in the feld, 

2010 the S11 of the antenna was measured just before connection to the fnal system to ensure the antenna’s environmental 

radiation pattern was accounted for. 

7.3.2 Software Verifcation 

In the lab, prior to deployment, the spectrum-characterization and occupancy sensing (SCOS) software was used to 

run a number of actions to get a distribution of trends for each sensor. The sensor was connected end-to-end with 

2015 exception of the antenna, which is either replaced with the probe or left unterminated for calibrations and the software 

was controlled locally through a breakout Ethernet connection. 

First, the calibration action was run ten (10) times and the resulting compiled calibration fles are recovered from the 

sensor. This was used to determine an expected spread of calibration values. The resultant calibration trend was used 

to compare with post deployment calibrations to ensure the functionality up to the preselector had remained intact. An 

2020 onboard calibration was taken at several points during the installation process up to the point when the preselector was 

mounted. The feld data was verifed to be similar to the lab-taken data, see Figure 7.9. 

88 
DRAFT

3560 3580 3600 3620 3640 3660 3680 3700

Frequency (MHz)

30.0

30.5

31.0

31.5

32.0

Gain (dB)

Comparison of sensor calibrated gain in lab and onsite

GMM In Lab

HU In Lab

NIT In Lab

NIT Onsite

HU Onsite

GMM Onsite

Figure 7.9: Gain Calibration in the Laboratory and as Deployed. Each unit had 10 calibration actions performed in 

rapid succession in the laboratory, the solid line is the median of those calibration while the shaded region is 2σ. The 

solid squares show the response in as installed for 1 calibration action. 

89 
DRAFT

3560 3580 3600 3620 3640 3660 3680 3700

Frequency (MHz)

4.0

4.1

4.2

4.3

4.4

4.5

4.6

4.7

4.8

Noise Figure (dB)

Comparison of sensor calibrated noise figure in lab and onsite

GMM In Lab

HU In Lab

NIT In Lab

NIT Onsite

HU Onsite

GMM Onsite

Figure 7.10: Noise Figure Calibration in the Laboratory and as Deployed. Each unit had 10 calibration actions 

performed in rapid succession, the solid line is the median of those calibration while the shaded region is 2σ. The 

solid squares show the response in as installed for 1 calibration action. 

90 
DRAFT 

Similarly, with the above-mentioned calibration probe attached with a fxed attenuation, the data product is taken ten 

(10) times to get a distribution for the data product as well. Once the sensor is installed, the calibration probe is 

attached at the antenna port and the data product is taken. It is fnally verifed that the feld-measured data is within 

the expected range from the lab-taken data to ensure a successful installation. 

3560 3580 3600 3620 3640 3660 3680 3700

Frequency (MHz)

74.0

73.5

73.0

72.5

72.0

Power (dBm)

mean_power:NIT In Lab

mean_power:UH In Lab

mean_power:NIT Onsite

mean_power:UH Onsite

Figure 7.11: Measurement of a programmable noise source inside of the laboratory and at the installation sites.The 

solid line is the median of ten (10) measurements performed in a laboratory environment, with the shaded region representing the expanded uncertainty (2σ). The squares were measured post installation in an uncontrolled environment. 

2025 

7.3.3 System Electrical Power and Communication Verifcation 

In the laboratory before installation, if the system presented results consistent with the previous characterizations, 

the system electrical power was considered fully functioning. However, if there was an issue, there are diagnostic 

adaptors for each of the connectors and test points throughout the system to test for electrical power problems. Even 

2030 in full working order, it was still verifed that all web-relays could be cycled (with audible feedback if applicable). 

A fnal check of the power system was performed to ensure that the the AC web-switch could be cycled and that the 

controlling computer powered back up once power was restored. 

Finally, the data backhaul connection was tested by connecting to it through the local network. It was ensured that 

each of the 3 switches could be remotely controlled, the router could be accessed, and that the controller computer 

2035 could be remotely controlled. This was repeated in the feld and the systems were registered through SCOS manager 

to ensure there was a consistent data backhaul for each sensor. 

91 
2040

2045

2050

2055

2060

2065

2070

DRAFT 

Chapter 8 

Metrology Sensor Demonstration 

8.1 Objectives 

The National Advanced Spectrum and Communications Test Network (NASCTN) team has deployed three prototype 

Rev 3 sensors in order to demonstrate the effcacy of the approach and systems design described in this test plan. One 

prototype sensor was deployed locally in Boulder, Colorado in early October 2022. Two additional prototype sensors 

were deployed in the Hampton Roads region of coastal Virginia in late October 2022. Deployment of these prototype 

sensors has provided real-world data payloads for analysis. This has also served as a demonstration of the sensor 

installation process, and as a test case for remote data backhaul links. The data collected has been used to inform 

iterations on sensor hardware, software design, edge compute products, and system uncertainty characterization. In 

all cases, sensor installation went smoothly and all three sensors have been able to provide usable data payloads. 

Previously unforeseen error modes were identifed through long-term sensor operation, and have since been addressed 

through software updates pushed to the sensors. To identify locations for deployment of prototype sensors, site surveys 

were carried out in advance of sensor installation. 

8.2 Site Surveys 

8.2.1 Background 

Site surveys were performed in order to identify and evaluate potential sensor deployment locations for the NASCTN 

Sharing Ecosystem Assessment (SEA) project. These surveys aimed to identify infrastructure supporting sensor requirements, such as access to power and antenna mounting points, as well as to provide initial assessments of the 

radio frequency (RF) environments around potential deployment sites. A portable measurement system was designed 

for the purpose of conducting RF spectrum measurements in the Citizens Broadband Radio Service (CBRS) band 

during site surveys. This system had a nominal front end gain of 25 dB and a nominal noise fgure of 4.5 dB in the 

CBRS band. The surveys also provided opportunities to interface with area managers to discuss the required processes 

and logistics for sensor deployment. Using the results from these surveys in corroboration with modeling-informed 

analysis, NASCTN is to select suitable sites for sensor deployment in order to meet SEA test goals. 

8.2.2 Locations Visited 

Marine Corps Base Camp Pendleton was selected for surveying due to its status as an always-on ground-based 

Dynamic Protection Area (DPA) (GB-DPA). NASCTN team members from National Institute of Standards and Technology (NIST), the Institute for Telecommunication Sciences (ITS), and The MITRE Corporation (MITRE) arrived at 

Marine Corps Base Camp Pendleton on March 16, 2022, and conducted site surveys over the following three days. In 

that time period, 22 sites were visited and surveyed for potential deployment. Figure 8.1 shows a map of the surveyed 

sites. 

Various locations in coastal Virginia and North Carolina were visited, spanning approximately 85 miles North-toSouth from Cape Charles to Kill Devil Hills. The locations were selected due to their proximity to the East 1 E-DPA 

The majority of surveyed locations were concentrated in the Hampton Roads metropolitan area. NASCTN team 

92 
DRAFT

Figure 8.1: Sites surveyed at Marine Corps Base Camp Pendleton from March 16 to March 19, 2022. The Camp 

Pendleton GB-DPA is shown outlined in light blue, and the nearby West 14 Environmental Sensing Capability (ESC)-

monitored DPA (E-DPA) is outlined in red. All markers indicate surveyed sites, while markers with star icons embedded indicate surveyed sites where RF signals were recorded in the CBRS band above the sensor noise foor. 

members from NIST, ITS, and MITRE arrived in the area on March 28, 2022, and conducted site surveys over the 

following four days. In that time period, eight sites were visited and surveyed for potential deployment. Visited sites 

included locations operated by the United States Navy (USN), the National Park Service (NPS), the United States Fish 

2075 and Wildlife Service (USFWS), the National Aeronautics and Space Administration (NASA), the Virginia Marine 

Resources Commission, and the Virginia Port Authority (VPA). Due to only observing CBRS signals at one location, 

another potential deployment was identifed following the March 2022 surveys in Virginia and North Carolina. A 

building belonging to Hampton University (HU) was identifed as a potential prototype deployment site, and surveyed 

by a NASCTN team member from NASA Langley Research Center (LaRC) on September 13, 2022. The survey for 

2080 this location made use of a slightly modifed, but comparably performant, measurement system due to local equipment 

availability. Signals were observed in the CBRS band from this location. Figure 8.2 shows a map of the surveyed sites, 

both in March and September 2022. In the fgure, the cluster of 3 overlapping markers are various sites at the NASA 

Langley Research Center. 

8.2.3 Survey Methods 

2085 Both qualitative and quantitative analyses were performed to evaluate each visited site for sensor deployment feasibility. These were driven by expected requirements for a sensor deployment, which include access to alternating 

current (AC) power, strong long-term evolution (LTE) connection for data backhaul, and proper physical infrastructure to support the antenna and sensor. Pictures were taken at each site, and available facilities and site access details 

93 
DRAFT

Figure 8.2: Sites surveyed near the East 1 E-DPA from March 28 to March 31, 2022. The East 1 E-DPA is shown 

outlined in red, the Dam Neck GB-DPA is outlined in light blue, and the St. Inigoes CBRS exclusion zone is outlined 

in yellow. All markers indicate surveyed sites, while markers with star icons embedded indicate surveyed sites where 

RF signals were recorded in the CBRS band above the sensor noise foor. Blue markers indicate sites surveyed in 

March 2022; the red marker indicates the site surveyed in September 2022. 

were documented. Cellular uplink and downlink speed tests were performed to evaluate LTE backhaul potential. 

2090 8.2.4 RF Survey Results 

The primary purpose of the RF surveys at each site was to identify the presence of signals in the CBRS band. The 

measurement system was calibrated using the Y-Factor method at the start of each day when survey measurements 

were performed. As shown in Figure 8.1, eleven of the surveyed sites at Camp Pendleton indicated the presence of at 

least one signal of any type with power above the noise foor of the survey measurement system. In most cases, these 

2095 signals were low-level, and in some cases, the signals appeared to be incumbent radar systems. As shown in Figure 8.2, 

surveys recorded signals in the CBRS band at only one site on the East Coast. At this site, the signals appeared to 

wireless broadband cellular downlink signals, populating the entire band with power levels substantially above the 

sensor noise foor. Overall, the surveyed sites at which stronger CBRS signals were recorded were considered more 

preferable for sensor deployments, compared to sites in which no signals were recorded. 

2100 8.2.5 Camp Pendleton Deployment 

The GB-DPA Camp Pendleton is to be the frst deployment location for the NASCTN SEA sensors. In the survey of 

22 sites that covered the available areas at Camp Pendleton, the sites were evaluated in terms of: 

• Spectrum and in-phase and quadrature (IQ) captures around CBRS band 48 

• Observations of any signals indicating incumbents nearby 

2105 • Cellular throughput for backhaul use (outside band 48) 

• AC power and shelter availability 

• Altitude measurements 

94 
DRAFT 

Five sites were initially down-selected from the original 22 with the highest likely hood of being useful, which were 

later refned to four as seen in Figure 8.3. Of these, offcial requests have been submitted to Camp Pendleton for 

2110 installation of SEA sensors at two sites (shown in green), and are currently under evaluation and processing by the 

military installation: 

• Site ID 3 (∼570ft altitude). Notable features: 

– Co-located near Navy sensor 

– High probability of securing site 

2115 – Power available 

– Strong CBRS signal level measured at 3m above ground level (AGL). 

• Site ID 9 (∼300ft altitude). Notable features: 

– Structure with power nearby 

– Several options for mounting sensor system 

2120 – Limited CBRS signal level measured at 3m AGL but good exposure to north San Diego County 

Figure 8.3: Map of the down-selected sites from the GB-DPA Camp Pendleton along with the surveyed spectrum from 

one of the sites. The sites in green (3 and 9) have been submitted to Camp Pendleton for consideration for deployment. 

A subsequent survey to Camp Pendleton will be scheduled to confrm deployment details for the two requested sites 

(3 and 9) as well as further investigation into the two other down-selected sites (1 and 12) and other sites as identifed 

through modelling and community input. After this survey, as locations become approved and the next revision of 

sensor becomes available, sensors will be deployed to the approved sites and will be confgured to begin persistent 

2125 data collection in the CBRS band. Continual monitoring of the incoming data as well as continued evaluation of 

the modelled environment will allow periodic evaluation of sensor locations, including if additional sensors/sites are 

required to sample the GB-DPA Camp Pendleton. 

95 
DRAFT 

Table 8.1: Deployment parameters for the three prototype sensors. 

Parameter GMM Sensor NIT Sensor HU Sensor 

Latitude 39.99180° 36.91552° 37.02578° 

Longitude -105.2745° -76.32245° -76.34120° 

Azimuth 12° ± 5° N/A N/A 

Elevation 0° ± 1° 0° ± 3° 0° ± 3° 

AGL Estimate 4.09 m ± 0.05 m 45 m 65.5 m 

AMSL (WGS-84) 1783.4 m ± 1.5 m 48.5 m ± 1.5 m 70.4 m ± 1.5 m 

Antenna KP-3SX4-90 HG3509U-PRO HG3509U-PRO 

2130 

2135 

2140 

2145 

2150 

2155 

8.3 CBRS-ecosystem observations 

Prototype Rev3 sensors were deployed in three locations: one in Green Mountain Mesa (GMM) in Boulder, CO, and 

two in the Norfolk/Hampton, VA area at the Norfolk International Terminal (NIT) tower and HU building. These sensors represent live deployments and offer the opportunity to exercise the sensors in live environments, e.g. formulating 

data products on the sensors, ensuring a reliable backhaul connection, and failure mode analysis. 

When deploying, the signal processing unit (SPU) was placed in an indoor, sheltered location and cabling was run to 

the preselector installed outdoors near the antenna mount to minimize the cable loss to the antenna. The installation 

parameters were recorded (Table 8.1) where the heights were recorded as both AGL above mean sea level (AMSL) 

(World Geodetic System 1984 (WGS-84)). 

As the systems were deployed, calibration actions were run at several points and compared to lab calibrations to ensure 

that the system did not have any faults as it was being assembled and mounted. Once the installation was complete, 

the calibration probe described in subsection 7.3.1 was used to perform a fnal measurement at the antenna cable. This 

measurement was then compared to the lab results to ensure the system displayed similar performance. Finally, the 

S11 of the as-deployed antenna was taken so the impedance difference between the calibration probe and the antenna 

could be accounted for. 

Once the systems were fully deployed, the backhaul connectivity was tested. First, the signal strength indicator 

was used to position the cellular router and its antennas in the case of the VA sensors. Then, coming in through 

the laboratory network, it was ensured that each component (computer, preselector, webswitch, etc.) was accessible 

remotely. A calibration action was then run followed by a data product action to ensure data was remotely accessible. 

The results of the data product action were also used to compare with prior site survey results for the location. Finally, 

the sensors were registered in spectrum-characterization and occupancy sensing (SCOS) manager to enable the tasking 

of calibration actions on an hourly basis and data product actions with data retrieval. 

8.3.1 Green Mountain Mesa, Boulder, CO 

The frst prototype sensor was deployed locally at GMM near the Department of Commerce (DoC) Boulder laboratories campus. This nearby location offered an easily managed sensor where repair or revision turnaround was relatively 

short and failure modes could be identifed and rectifed quickly. This allowed it to be used a development sensor, 

pushing software updates or revising hardware and could be fxed in a relatively short manner if a problem occurred. 

In addition, this sensor retained a hardline backhaul to the network. This removed any data or bandwidth limitations from the sensor, allowing much more frequent software updates or the capture of raw-form data for testing data 

products. 

The deployment of this prototype sensor offered the ability to exercise the installation plan, including ensuring all 

96 
DRAFT 

necessary equipment was included. Throughout the installation, failure modes and solutions were recorded, e.g. 

2160 ensuring the RF tray has power after a power cycle. It was also ensured that each installation hurdle had a solution 

that was remotely confgurable to ensure that future deployments would not run into prolonged periods of down time 

due to these installation challenges. 

Figure 8.4: Image of deployed prototype Rev 2 and 3 sensors at the GMM site in Boulder, CO. The close proximity to 

Boulder labs makes this an ideal location to test prototypes before fully remote deployments. 

8.3.2 East 1 E-DPA 

Following the surveys near the East 1 E-DPA, prototype sensors were also deployed to two locations in the Norfolk 

2165 and Hampton, VA areas. The two sensors were installed in late October, 2022. The frst was installed atop a tower 

operated by the Virginia Port Authority within the NIT. The second was installed atop a building operated by HU. 

The two sites are shown in Figure 8.2 as the blue and red markers with embedded star icons, respectively. Both of 

these locations offered high-elevation vantage points above the surrounding area. Site surveys indicated strong CBRS 

occupancy from both locations. Deployment of these prototype sensors included the full system, including remote 

2170 setup and operation as well as the cellular backhaul to the central server. 

When frst installed, the sensors exhibited approximately 90% data success rate. Of the data product and calibration 

actions, 90% succeeded and returned valid data. After analysis of failures, incremental updates were devised. These 

were frst tested on the GMM deployment, then were rolled out to the VA sensors once stable. After the most recent 

software updates, NASCTN has not encountered additional failure modes over the most recent three week period. 

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(a) Sensor staged for deployment (b) Preselector deployed at NIT (c) Deployed sensor at HU 

Figure 8.5: Pictures of the prototype sensors deployed in Virginia near the East 1 DPA. 

2175 These sensors offer the opportunity to take preliminary data incorporating both CBRS and the incumbent. The proximity to the Norfolk harbor and East 1 DPAs has the potential for observing incumbent signals as well as the dynamics 

of the CBRS ecosystem, such as in Figure 6.17. While the focus in this work is on GB-DPAs, the specifc Camp 

Pendleton DPA is in close proximity to the West 14 DPA and will likely observe the dynamics of the CBRS ecosystem 

in addition to the protections allotted to GB-DPAs. 

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2180 Chapter 9 

Conclusions 

The goal of this National Advanced Spectrum and Communications Test Network (NASCTN) test plan is multi-fold. 

To that end, some important elements from the test plan include the following: 

• The test plan, though specifc to the ground-based Dynamic Protection Area (DPA) (GB-DPA) at Camp Pendle2185 ton, encompasses a uniform sensor architecture that is anticipated to be deployed across the Sharing Ecosystem 

Assessment (SEA) project task elements 

• This test plan document serves as a reference for upcoming NASCTN SEA test plans. 

• We anticipate, pending adjudicated commentary, the following to stay largely the same across test plans: 

1. Sensor RF system architecture 

2190 2. spectrum-characterization and occupancy sensing (SCOS) based sensor management 

3. site survey methodology 

4. blueprint for sensor system validation, acceptance testing and calibration 

• NASCTN is still investigating and developing towards the ingestion and validation of data at scale. The edge 

compute data products highlighted in this document are considered pre-decisional draft data. 

2195 • Modeling and simulation heavily infuenced by the Wireless Innovation Forum (WInnForum) reference implementation, which is based on the Irregular Terrain Model (ITM). This does not constitute an endorsement by NASCTN 

of any particular propagation model but exemplifes a methodology towards incorporating modeling and simulation 

into the data driven insights needed to quantify effects in the Citizens Broadband Radio Service (CBRS) ecosystem. 

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