Signal Processing for Joint Radar Communications E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley - IEEE
- Sprache: Englisch
Signal Processing for Joint Radar Communications A one-stop, comprehensive source for the latest research in joint radar communications In Signal Processing for Joint Radar Communications, four eminent electrical engineers deliver a practical and informative contribution to the diffusion of newly developed joint radar communications (JRC) tools into the sensing and communications communities. This book illustrates recent successes in applying modern signal processing theories to core problems in JRC. The book offers new results on algorithms and applications of JRC from diverse perspectives, including waveform design, physical layer processing, privacy, security, hardware prototyping, resource allocation, and sampling theory. The distinguished editors bring together contributions from more than 40 leading JRC researchers working on remote sensing, electromagnetics, optimization, signal processing, and beyond 5G wireless networks. The included resources provide an in-depth mathematical treatment of relevant signal processing tools and computational methods allowing readers to take full advantage of JRC systems. Readers will also find: * Thorough introductions to fundamental limits and background on JRC theory and applications, including dual-function radar communications, cooperative JRC, distributed JRC, and passive JRC * Comprehensive explorations of JRC processing via waveform analyses, interference mitigation, and modeling with jamming and clutter * Practical discussions of information-theoretic, optimization, and networking aspects of JRC * In-depth examinations of JRC applications in cutting-edge scenarios including automotive systems, intelligent reflecting surfaces, and secure parameter estimation Perfect for researchers and professionals in the fields of radar, signal processing, communications, information theory, networking, and electronic warfare, Signal Processing for Joint Radar Communications will also earn a place in the libraries of engineers working in the defense, aerospace, wireless communications, and automotive industries.
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
Dedication
List of Editors
List of Contributors
Foreword
Preface
Acknowledgments
Part I: Fundamental Limits and Background
1 A Signal Processing Outlook Toward Joint Radar‐Communications
1.1 Introduction
1.2 Policy and Licensing Issues
1.3 Legal Challenges
1.4 Agency‐Driven Projects
1.5 Channel Considerations
1.6 JRC Coexistence
1.7 JRC Co‐Design
1.8 Emerging JRC Applications
1.9 Open Problems and Summary
References
2 Principles of Radar‐Centric Dual‐Function Radar‐Communication Systems
2.1 Background
2.2 DFRC System Model
2.3 DFRC Using Fixed Radar Waveforms
2.4 DFRC Using Modulated Radar Waveforms
2.5 DFRC Using Index Modulation
2.6 Challenges and Future Trends
References
3 Interference, Clutter, and Jamming Suppression in Joint Radar–Communications Systems – Coordinated and Uncoordinated Designs
3.1 Introduction
3.2 Joint Design of Coordinated Joint Radar–Communications Systems
3.3 Interference Suppression in Uncoordinated Joint Radar–Communications Systems
3.4 Conclusion
References
Notes
4 Beamforming and Interference Management in Joint Radar–Communications Systems
4.1 Introduction
4.2 System Overview
4.3 JRC Beamforming
4.4 Multicarrier Waveforms for JRC
4.5 Precoder Design for Multiple JRC Users
4.6 Summary
List of Symbols
References
5 Information Theoretic Aspects of Joint Sensing and Communications
5.1 Introduction
5.2 Information Theoretic Model
5.3 Fundamental Trade‐off Between Sensing and Communications
5.4 Application to Joint Radar and Communications
5.5 Concluding Remarks
5.A Proof of Theorem 5.1
5.B Proof of Theorem 5.2
Acknowledgment
References
Notes
Part II: Physical‐Layer Signal Processing
6 Radar‐aided Millimeter Wave Communication
6.1 Motivation for Radar‐aided Communication
6.2 Radar‐aided Communication Exploiting Position Information
6.3 Radar‐aided Communication Exploiting Covariance Information
6.4 Challenges and Opportunities
References
7 Design of Constant‐Envelope Radar Signals Under Multiple Spectral Constraints
7.1 Introduction
7.2 System Model and Problem Formulation
7.3 Radar Waveform Design Procedure
7.4 Performance Analysis
7.5 Conclusion
7.A Appendix
References
Notes
8 Spectrum Sharing Between MIMO Radar and MIMO Communication Systems*
8.1 Introduction
8.2 MIMO Radars Using Sparse Sensing
8.3 Coexistence System Model
8.4 Cooperative Spectrum Sharing
8.5 Numerical Results
8.6 Conclusions
References
Note
9 Performance and Design for Cooperative MIMO Radar and MIMO Communications
9.1 Introduction and Literature Review
9.2 Cooperative CERC System Model
9.3 Hybrid Active–Passive Cooperative CERC MIMO Radar System
9.4 Radar‐aided MIMO Communications in Cooperative CERC System
9.5 Cooperative Radar and Communications System Co‐design
9.6 Conclusions
References
Part III: Networking and Hardware Implementations
10 Frequency‐Hopping MIMO Radar‐based Data Communications
10.1 Introduction
10.2 System Diagram and Signal Model
10.3 Practical FH‐MIMO DFRC
10.4 Discussion
References
Notes
11 Optimized Resource Allocation for Joint Radar‐Communications
11.1 Introduction
11.2 Single Transmitter‐Based JRC System
11.3 Transmit Array‐Based JRC System
11.4 Distributed JRC System
11.5 Conclusions
References
12 Emerging Prototyping Activities in Joint Radar‐Communications
12.1 Motivation
12.2 Prototyping: General Principles and Categorization
12.3 JRC Prototypes: Typical Features and Functionalities
12.4 JRC Prototyping
12.5 Coexistence JRC Prototype
12.6 Other JRC Prototypes
12.7 Conclusion
References
Notes
13 Secrecy Rate Maximization for Intelligent Reflective Surface‐Assisted MIMO Communication Radar
13.1 Introduction
13.2 System Model
13.3 System Optimizations
13.4 Simulation Results
13.5 Conclusion
13.A Appendix
References
Note
14 Privacy in Spectrum Sharing Systems with Applications to Communications and Radar
14.1 Introduction
14.2 Spectrum Sharing Systems
14.3 User Privacy in Spectrum Sharing
14.4 Optimal Privacy and Performance
14.5 Practical Privacy Preservation
14.6 Spectrum Sharing Case Studies with Radar Primary Users
14.7 Summary and Future Directions
References
Notes
Epilogue
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Co‐existing radar systems and communications services at different...
Table 1.2 Comparison of mmWave and THz transmission characteristics [Elbir e...
Table 1.3 A comprehensive classification of JRC systems
Chapter 3
Table 3.1 Symbol definitions for joint design.
Table 3.2 Symbol definitions for stepped‐frequency MIMO radar.
Chapter 5
Table 5.1 Simulation parameters.
Chapter 6
Table 6.1 System parameters for the simulation of a radar‐aided mmWave vehic...
Table 6.2 System parameters for the simulation of a radar‐aided mmWave vehic...
Chapter 8
Table 8.1 The radar ESINR, MC relative recovery errors, and the relative tar...
Chapter 10
Table 10.1 Critical information for PSK demodulation in FH‐MIMO DFRC.
Table 10.2 Waveform constraints for LoS FHX‐MIMO DFRC.
Table 10.3 Coherent accumulation estimator (CAE) for estimating .
Table 10.4 Chinese remainder theorem estimator (CRE) for estimating .
Table 10.5 Simulation parameters.
Chapter 11
Table 11.1 Power allocation for distributed JRC system for ...
Chapter 12
Table 12.1 Design aspects for prototyping.
Table 12.2 Examples of wireless communication prototypes.
Table 12.3 Typical components of radar prototyping.
Table 12.4 Hardware characteristics of the proposed prototype.
Table 12.5
LTE
features of the considered application framework.
Table 12.6 Features of the developed cognitive MIMO radar.
Table 12.7 Radar experiment parameter setup.
Table 12.8 Radar target parameter setup.
Table 12.9 Communication link parameter setup.
Table 12.10 SpaCor prototype functionalities.
Chapter 14
Table 14.1 Notation.
List of Illustrations
Chapter 1
Figure 1.1 Comparison of millimeter‐wave and THz band characteristics for JR...
Figure 1.2 (a) Spectral coexistence system where radar and communications su...
Figure 1.3 A simplified block diagram showing major steps of transmit and re...
Figure 1.4 The AFs of bi‐static mmWave JRC using (a) OFDMA and (b) PMCW sign...
Figure 1.5 The root‐mean‐square‐error (RMSE) of the estimated range of a sin...
Figure 1.6 Radar signatures generated from animation models of (a) a small c...
Figure 1.7 Power allocation solutions for JRC carrier exploitation via (a) w...
Chapter 2
Figure 2.1 Illustrative diagram of an DFRC system.
Figure 2.2 Transmit beampattern with two distinct SLLs toward the communicat...
Figure 2.3 Probability distribution of the magnitude of signal at output of ...
Figure 2.4 Bit error rate versus SNR for three beampattern modulation method...
Figure 2.5 Symbol error rate versus spatial angle of the communication recei...
Figure 2.6 Bit error rate of binary CPM phase‐attached to a LFM radar wavefo...
Figure 2.7 Power spectral density of the base radar waveform (dB) and CPM‐ba...
Figure 2.8 Symbol error rate versus SNR.
Figure 2.9 AF zero Doppler cut for a series of 10 pulses of FH waveforms wit...
Figure 2.10 AF zero Doppler cut for a series of 10 pulses of FH waveforms wi...
Figure 2.11 Symbol error rate versus SNR.
Chapter 3
Figure 3.1 Example of a MIMO communications system coexisting with a mono‐st...
Figure 3.2 Signal received by the radar over the first transmit period.
Figure 3.3 Signal received by the communication RX over the first PRI.
Figure 3.4 Doppler‐azimuth plane of CAF at for for iteration number .
Figure 3.5 (a) versus ...
Figure 3.6 Frequency occupation versus time for two representative spectrum ...
Figure 3.7 Data flow graph for ADMM‐Net.
Figure 3.8 (a) Test set
normalized mean squared error
(
NMSE
) versus iteratio...
Figure 3.9 Recovered range‐velocity image slice for ADMM (a) and ADMM‐Net (b...
Chapter 4
Figure 4.1 Co‐design, cooperation, and noncooperative users in a JRC system....
Figure 4.2 Beamforming in JRC system. The beampattern is designed so that th...
Figure 4.3 JRC transmit beampattern example with power modulation using diff...
Figure 4.4 Receiver filter example using matched and mismatched filtering. T...
Figure 4.5 Block diagram of OFDM joint radar and communications system.
Figure 4.6 Subcarrier assignment for radar or communications users. Communic...
Figure 4.7 An example of radar‐centric design and obtained water‐filling pow...
Figure 4.8 Baseband transmitter model of proposed joint radar–communication ...
Figure 4.9 Zero‐Doppler cut and AF plots for MC‐DS‐CDMA joint radar–communic...
Figure 4.10 System model for cooperative JRC system. The regular lines and t...
Figure 4.11 Comparison between rank‐constrained rank minimization (RCRM) and...
Chapter 5
Figure 5.1 Information theoretic model for joint state sensing and communica...
Figure 5.2 Capacity–distortion trade‐off of the binary channel with Bernoull...
Figure 5.3 Capacity–distortion trade‐off of the Rayleigh fading channels wit...
Figure 5.4 Joint radar sensing and communications model.
Figure 5.5 RMSE of the target range estimation versus .
Figure 5.6 RMSE of the target velocity estimation versus .
Chapter 6
Figure 6.1 MIMO V2I communication set up with an active BS mounted radar tha...
Figure 6.2 The spectral efficiency versus transmit power ...
Figure 6.3 (a) Over the air azimuth power spectra. (b) Observed power spectr...
Figure 6.4 Intuitive explanation of the similarity metric (6.5).
Figure 6.5 Prototype of a radar assisted V2I link based on two INRAS Radarbo...
Figure 6.6 Similarity metric as a function of evaluated in an outdoor urba...
Figure 6.7 MIMO V2I communication set up with a BS mounted passive radar rec...
Figure 6.8 Rate versus the number of beams for and in NLOS channel.
Figure 6.9 DNN architecture for learning the communication covariance vector...
Figure 6.10 DNN architecture for learning the dominant eigenvector of the co...
Figure 6.11 The DNN architecture for learning the communication APS.
Figure 6.12 Example of the estimated communication APS from the translation ...
Figure 6.13 CDF of the similarity between the estimated and the true communi...
Figure 6.14 Rate results with the beam search size of 64 for exhaustive sear...
Chapter 7
Figure 7.1 A pictorial representation of the spectrally crowded scenario und...
Figure 7.2 Radar probing waveform with fast‐time phase coding.
Figure 7.3 A pictorial representation of the REM and its usage in radar. Sou...
Figure 7.4 versus (in dB) of codes synthesized...
Figure 7.5 Achieved and the transmitted energy versus of the continuous ...
Figure 7.6 ESD versus normalized frequency of the phase codes designed for
Figure 7.7 Normalized ACF of the phase codes designed for . (a) Continuous ...
Figure 7.8 Achieved versus the iteration number with for the continuous ...
Chapter 8
Figure 8.1 A collocated MIMO‐MC radar system using random sampling at the re...
Figure 8.2 A MIMO communications system sharing spectrum with a MIMO radar s...
Figure 8.3 Radar–communication coexistence signal model during one radar PRI...
Figure 8.4 The spectrum sharing architecture. The cooperation is coordinated...
Figure 8.5 TDM‐based CSI estimation and feedback and reception of design res...
Figure 8.6 The subsampling at the radar receiver modulates the interference ...
Figure 8.7 The radar transmit beampattern and the MUSIC spatial pseudo‐spect...
Figure 8.8 Comparison of spectrum sharing with different levels of cooperati...
Figure 8.9 Comparison of spectrum sharing with adaptive and constant‐rate co...
Figure 8.10 Comparison of spectrum sharing with traditional MIMO radars and ...
Chapter 9
Figure 9.1 An example DFRC system.
Figure 9.2 An example CERC system.
Figure 9.3 Popular research topics on integrated radar and communications sy...
Figure 9.4 A cooperative MIMO radar and MIMO communications system.
Figure 9.5 Parameter setup for cooperative CERC system with ...
Figure 9.6 Parameter setup for cooperative CERC system with ...
Figure 9.7 Target detection probability ((9.20) and (9.33)) versus SCNR fo...
Figure 9.8 Target detection probability ((9.20) and (9.33)) versus SCNR fo...
Figure 9.9 Target detection probability ((9.20) and (9.33)) versus SCNR fo...
Figure 9.10 Target localization RCRB ((9.29) and (9.39)) versus SCNR for the...
Figure 9.11 Target localization RCRB ((9.29) and (9.39)) versus SCNR for the...
Figure 9.12 Target localization RCRB ((9.29) and (9.39)) versus SCNR for the...
Figure 9.13 Communications mutual information (MI) ((9.50) and (9.52)) versu...
Figure 9.14 Communications mutual information (MI) ((9.50) and (9.52)) versu...
Figure 9.15 Communications mutual information (MI) ((9.50) and (9.52)) versu...
Figure 9.16 Comprehensive performance (9.55) versus for a cooperative CERC...
Figure 9.17 Comprehensive performance (9.55) versus for a cooperative CERC...
Figure 9.18 Comprehensive performance (9.55) versus for a cooperative CERC...
Figure 9.19 Comprehensive performance (9.56) versus for a cooperative CERC...
Figure 9.20 Comprehensive performance (9.56) versus for a cooperative CERC...
Figure 9.21 Comprehensive performance (9.56) versus for a cooperative CERC...
Chapter 10
Figure 10.1 (a) Illustration on the system diagram of an FH‐MIMO DFRC. (b) T...
Figure 10.2 Illustration of timing offset at UE, where the UE‐sampled hops a...
Figure 10.3 MSE of estimation against , where dash curves are MSELBs corr...
Figure 10.4 MSE of ...
Figure 10.5 MSE of ...
Figure 10.6 SER performance, where SER0 denotes the SER evaluated based on t...
Figure 10.7 Illustration of the severe signal attenuation caused by multipat...
Chapter 11
Figure 11.1 Illustration of three different types of JRC systems: (a) single...
Figure 11.2 Illustrative example of subcarrier allocation and power distribu...
Figure 11.3 Normalized channel gains for target and CUs.
Figure 11.4 Radar‐centric design of single transmitter‐based JRC system. (a)...
Figure 11.5 Cooperative design of single transmitter‐based JRC system. (a) M...
Figure 11.6 Sensor selection for JRC system (“X” denotes unselected antenna ...
Figure 11.7 Resource allocation for individual beamformers. (a) Beamforming ...
Figure 11.8 Resource allocation for multiple beamformers. (a) Beamforming ga...
Figure 11.9 Simulation layout for distributed JRC system.
Chapter 12
Figure 12.1 Typical coexistence architectures depicting the mode of enforcin...
Figure 12.2 Components or application frameworks of the prototype:
LTE
appli...
Figure 12.3 A photograph of the proposed coexistence prototype. The photo sh...
Figure 12.4 Developed cognitive
MIMO
radar application: (a) user interface f...
Figure 12.5 Block diagram of the developed cognitive
MIMO
radar transmitter....
Figure 12.6 In (a), the matched filter coefficients are updated for appropri...
Figure 12.7 Diagram depicting the connection of the JRC coexistence prototyp...
Figure 12.8 Schematic depicting target generation [Alaee‐Kerahroodi et al., ...
Figure 12.9 (a) Validation of the developed spectrum sensor using a commerci...
Figure 12.10 (a) Radar is transmitting random‐phase sequences on the communi...
Figure 12.11 (a) MIMO radar utilizing optimized waveforms and not taking cog...
Figure 12.12 PDSCH throughput of
LTE
under radar interference for different ...
Figure 12.13 Target
SINR
under interference from downlink
LTE
link for diffe...
Chapter 13
Figure 13.1 IRS‐assisted MIMO radar–communications system.
Figure 13.2 Convergence behavior of the secrecy rate maximization problem.
Figure 13.3 Achieved secrecy rate against different maximum power limit for ...
Figure 13.4 Achieved secrecy rate against different SINR thresholds for the ...
Figure 13.5 Achieved secrecy rate against different number of IRS elements
Figure 13.6 Convergence behavior of the transmit power minimization problem....
Figure 13.7 Achieved transmit power against the different SINR thresholds ...
Figure 13.8 Achieved transmit power against different secrecy rate threshold...
Chapter 14
Figure 14.1 Spectrum sharing system models.
Figure 14.2 MIMO radar and MIMO communication system interference.
Figure 14.3 Adversary classifier ROC.
Figure 14.4 MI between precoders and radar positions for a varying number of...
Figure 14.5 Adversary estimated radar angle. The vertical line represents th...
Figure 14.6 Adversary estimation error against [a] the interference optimal ...
Figure 14.7 Interference power into the PU with [a] the interference optimal...
Figure 14.8 SU utility versus number of PU locations [error bars denote stan...
Figure 14.9 Topology and adversary estimates for fixed PUs. [a] Topology, [b...
Figure 14.10 Adversary estimates over time. [a] 4-km by 4-km cells and [b] 1...
Figure 14.11 Adversary estimates of mobile PUs [10-kph]. [a] Track history a...
Figure 14.12 Mutual information after SAS obfuscation.
Figure 14.13 Adversary error after PU obfuscation.
Figure 14.14 Example topology for CBRS case study.
Figure 14.15 SU utility versus average distance error in CBRS.
Figure 14.16 SU utility versus estimated mutual information in CBRS.
Figure 14.17 SU utility and PU privacy versus .
Guide
Cover
Table of Contents
Title Page
Copyright
Dedication
List of Editors
List of Contributors
Foreword
Preface
Acknowledgments
Begin Reading
Epilogue
Index
End User License Agreement
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Signal Processing for Joint Radar Communications
Edited by
Kumar Vijay Mishra, M. R. Bhavani Shankar, Björn Ottersten, and A. Lee Swindlehurst
Copyright © 2024 by John Wiley & Sons, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
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Library of Congress Cataloging‐in‐Publication Data:
Names: Mishra, Kumar Vijay, editor. | Shankar, M. R. Bhavani, editor. |
Ottersten, Björn, editor. | Swindlehurst, A. Lee, editor.
Title: Signal processing for joint radar communications / edited by Kumar
Vijay Mishra, M. R. Bhavani Shankar, Björn Ottersten, and A. Lee
Swindlehurst.
Description: Hoboken, NJ : Wiley, 2024. | Includes index.
Identifiers: LCCN 2024008702 (print) | LCCN 2024008703 (ebook) | ISBN
9781119795537 (hardback) | ISBN 9781119795544 (adobe pdf) | ISBN
9781119795551 (epub)
Subjects: LCSH: Signal processing. | Radar.
Classification: LCC TK5102.9 .S5426 2024 (print) | LCC TK5102.9 (ebook) |
DDC 621.382/2–dc23/eng/20240321
LC record available at https://lccn.loc.gov/2024008702
LC ebook record available at https://lccn.loc.gov/2024008703
Cover Design: Wiley
Cover Image: © Jackie Niam/Shutterstock
“Dedicated, with supreme reverence and humility, to Saraswati, goddess of wisdom and knowledge. To my Mom Shraddha Mishra and the memory of my Dad Shyam Bihari Mishra.” – K. V. M.
“To my late father Mr. M. N. Rama Rao.” – M. R. B. S.
“To the students I have had the privilege to work with.” – B. O.
“To the students, friends, and colleagues who have helped me on my lucky journey.” – A. L. S.
List of Editors
Kumar Vijay Mishra, United States CCDC Army Research Laboratory, Adelphi, MD, USA
M. R. Bhavani Shankar, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg
Björn Ottersten, KTH Royal Institute of Technology, Stockholm, Sweden
A. Lee Swindlehurst, University of California, Irvine, CA, USA
List of Contributors
Ammar Ahmed
Aptiv
Advanced Safety and User Experience
Agoura Hills, CA
USA
Tuomas Aittomäki
Aalto University
Department of Signal Processing and Acoustics
Espoo
Finland
Mohammad Alaee‐Kerahroodi
Interdisciplinary Centre for Security, Reliability and Trust (SnT)
University of Luxembourg
Luxembourg
Anum Ali
Standards and Mobility Innovation Laboratory
Samsung Research America
Plano, TX
USA
Moeness G. Amin
Villanova University
Center for Advanced Communications
Villanova, PA
USA
Augusto Aubry
University of Naples, Federico II
Department of Electrical and Information Technology Engineering
Napoli
Italy
Rick S. Blum
Electrical and Computer Engineering Department
Lehigh University
Bethlehem, PA
USA
Giuseppe Caire
Technical University of Berlin
Chair of Communications and Information Theory
Berlin
Germany
Jonathon A. Chambers
University of Leicester
School of Engineering
Leicestershire, Leicester
UK
Gaojie Chen
University of Surrey
5GIC & 6GIC, Institute for Communication Systems
Department of Electrical and Electronic Engineering (ICS)
Guildford
UK
Yun Chen
Department of Electrical and Computer Engineering
North Carolina State University
Raleigh, NC
USA
Matthew A. Clark
The Aerospace Corporation
El Segundo, CA
USA
Guolong Cui
University of Electronic Science and Technology of China
School of Information and Communication Engineering
Chengdu
China
Yuanhao Cui
Aalto University
Department of Signal Processing and Acoustics
Espoo
Finland
and
Beijing University of Posts and Telecommunications
School of Information and Communication Engineering
Beijing
China
Antonio DeMaio
University of Naples, Federico II
Department of Electrical and Information Technology Engineering
Napoli
Italy
Sisai Fang
University of Surrey, 5GIC & 6GIC
Institute for Communication Systems
Department of Electrical and Electronic Engineering (ICS)
Guildford
UK
Nuria González‐Prelcic
Department of Electrical and Computer Engineering
North Carolina State University
Raleigh, NC
USA
Yingjie J. Guo
University of Technology Sydney
Global Big Data Technologies Centre, and School of Electrical and Data Engineering
Faculty of Engineering and IT
Sydney, New South Wales
Australia
Aboulnasr Hassanien
Aptiv PLC
Radar Systems Group
Agoura Hills, CA
USA
Qian He
Yangtze Delta Region Institute Quzhou
University of Electronic Science and Technology of China
Zhejiang, Quzhou
China
and
Electronic Engineering Department
University of Electronic Science and Technology of China
Sichuan, Chengdu
China
Xiaojing Huang
University of Technology Sydney
Global Big Data Technologies Centre, and School of Electrical and Data Engineering
Faculty of Engineering and IT
Sydney, New South Wales
Australia
Jeremy Johnston
Columbia University
Electrical Engineering Department
New York, NY
USA
Mari Kobayashi
Technical University of Munich
Chair of Communications Engineering
Munich
Germany
Visa Koivunen
Aalto University
Department of Signal Processing and Acoustics
Espoo
Finland
Sangarapillai Lambotharan
Loughborough University
School of Mechanical, Manufacturing and Electrical Engineering
Leicestershire, Loughborough
UK
Bo Li
Aurora Innovation, Inc.
Research and Development Department
Pittsburgh, PA
USA
Kumar Vijay Mishra
United States CCDC Army Research Laboratory
Adelphi, MD
USA
Björn Ottersten
KTH Royal Institute of Technology
Stockholm
Sweden
Cunhua Pan
Queen Mary University of London
School of Electronic Engineering and Computer Science
London
UK
Athina P. Petropulu
Rutgers, the State University of New Jersey
Department of Electrical and Computer Engineering
Piscataway, NJ
USA
Konstantinos Psounis
University of Southern California
Los Angeles, CA
USA
Junhui Qian
Chongqing University
School of Microelectronic and Communication Engineering
Chongqing
China
M. R. Bhavani Shankar
Interdisciplinary Centre for Security, Reliability and Trust (SnT)
University of Luxembourg
Luxembourg
A. Lee Swindlehurst
University of California
Irvine, CA
USA
Xiaodong Wang
Columbia University
Electrical Engineering Department
New York, NY
USA
Zhen Wang
Yangtze Delta Region Institute Quzhou
University of Electronic Science and Technology of China
Zhejiang, Quzhou
China
and
Electronic Engineering Department
University of Electronic Science and Technology of China
Sichuan, Chengdu
China
Kai Wu
University of Technology Sydney
Global Big Data Technologies Centre, and School of Electrical and Data Engineering
Faculty of Engineering and IT
Sydney, New South Wales
Australia
Jing Yang
University of Electronic Science and Technology of China
School of Information and Communication Engineering
Chengdu
China
Xianxiang Yu
University of Electronic Science and Technology of China
School of Information and Communication Engineering
Chengdu
China
Jian A. Zhang
University of Technology Sydney
Global Big Data Technologies Centre, and School of Electrical and Data Engineering
Faculty of Engineering and IT
Sydney, New South Wales
Australia
Yimin D. Zhang
Temple University
Department of Electrical and Computer Engineering
Philadelphia, PA
USA
Junze Zhu
Yangtze Delta Region Institute Quzhou
University of Electronic Science and Technology of China
Zhejiang, Quzhou
China
and
Electronic Engineering Department
University of Electronic Science and Technology of China
Sichuan, Chengdu
China
Foreword
We congratulate the editors and authors on the timely publication of this book discussing the important problem of coexistence of radar and communication systems, as well as many related topics. The radiofrequency spectrum is a precious resource, which has become more and more congested in recent years. Let us take automotive radar as an example, which is capable of functioning in extreme adverse weather and light conditions: autonomous driving vehicles are equipped with ten or more radar sensors. Therefore, mutual interference among automotive radars is becoming increasingly severe, and the current practice is to disregard the parts of data that are corrupted. Interestingly, through joint radar and communication coordination, it may, in fact, be possible to translate mutual interference into collaboration by sharing information.
However, the coordination of radar and communications can be rather challenging in practical applications. For autonomous driving, for example, latency allowed for this coordination is extremely small. Moreover, for mass markets, radar sensors must be of low cost, which makes it difficult to use high‐precision converters at the transmitter and receiver over a wide frequency band. Furthermore, multi‐input multi‐out radars have become a standard in the automotive radar industry. However, to realize a large virtual aperture for high angular resolution, through exploiting diversity, waveform orthogonality requirements must be considered.
Dual‐function systems, which simultaneously support radar and communications, are the topic of this volume. Dual‐function systems have many advantages over traditional systems: the number of antennas and the radar cross section of the platform are reduced; the weight of the platform is decreased and maneuverability is enhanced; and last but definitely not least, mutual interference among nearby systems is minimized and spectral compatibility is improved. Due to their great potential, dual‐function systems have recently received considerable attention from both academia and industry, and researchers dealing with these systems in research and in practice will find this book to be a worthwhile addition to their library.
As innovative ideas and discoveries are generated in this new field of joint radar and communications, they can be adopted to tackle the challenges encountered in practical applications, including the aforementioned ones. This book is an excellent collection of recent advances on joint radar and communications systems, and it will, without doubt, inspire further research on and developments for addressing radiofrequency spectrum congestion.
Jian Li
University of Florida, Gainesville, Florida, USA
Petre Stoica
Uppsala University, Uppsala, Sweden
Preface
We are delighted to edit this new book, Signal Processing for Joint Radar and Communications, published by the prestigious Wiley‐IEEE Press. The book comprises 14 chapters that are written by exceptionally well‐qualified experts from academia and research laboratories across the globe. The focus of this book is on signal processing aspects of joint radar and communications (JRC)–one of the most actively researched problems across as many as 15 IEEE societies/communities. The chapters were selected by the editors, who have a combined experience of 120 years in prestigious research laboratories.
The future of spectrum access will be increasingly shared, dynamic, and secure. The International Telecommunication Union (ITU) began allocating radio bands at least as early as 1937. The IEEE Radar Band Standard 521, which has been maintained since 1976, follows the spectrum designation practices that originated during World War II. Over the interceding multiple decades of bandwidth expansion for sensing, navigation, wireless communications, timing, and positioning, policy planners and technologists have faced pressures of inefficient and excessively cautious use of the spectrum. Conventional spectrum sharing rules have been framed more for worst‐case scenarios than for an optimal utilization of available frequencies. This approach inevitably leads to conflicts because the electromagnetic spectrum is a scarce resource. In 2021, the US Federal Communications Commission (FCC) was sued by AT&T over FCC's allocation of 6–7 GHz for dynamic allocation of allowed channels to Wi‐Fi access points for indoor Wi‐Fi 7 protocol. The US Federal Aviation Administration (FAA) was recently embroiled in a highly public battle with the airline industry over the use of the C‐band for fifth‐generation (5G) wireless services near airports. At lower bands, the FCC has been challenged to update their propagation models for their TV‐band rulings so as to allow the reuse of TV bands for other services.
Consequently, sensing systems (radar, lidar, or sonar) that share the spectrum with wireless communications (radio‐frequency/RF, optical, or acoustical) and still operate without any significant performance losses have captured significant research interest. Although a large fraction of these bands remains underutilized, radars need to maintain constant access to these bands for target sensing and detection as well as to increase the spectrum to accomplish missions such as secondary surveillance, multi‐function integrated RF operations, communications‐enabled autonomous driving, and cognitive capabilities. On the other hand, the wireless industry's demand for spectrum for providing new services and accommodating a massive number of users with high data rate requirement continues to increase. The present spectrum is used very inefficiently due to its highly fragmented allocation. Emerging wireless systems such as commercial Long‐Term Evolution (LTE) communications technology, fifth‐generation (5G), WiFi, Internet‐of‐Things (IoT), and Citizens Broadband Radio Services (CBRS) are already causing spectral interference to legacy military, weather, astronomy, and aircraft surveillance radars. Similarly, radar signals in adjacent bands leak to spectrum allocated for communications and deteriorate the service quality. Therefore, it is essential and beneficial for radar and communications to develop strategies to simultaneously and opportunistically operate in the same spectral bands in a mutually beneficial manner.
The interference from other emitters and its mitigation has been of interest within IEEE for decades. Until the early 1960s, IEEE used to publish IEEE Transactions on Radio Frequency Interference led by IRE Professional Technical Group on Radio Frequency Interference. The journal ceased publication in 1963. The periodical Frontiers of Technology: Trends in Electronics Research by Mattraw and Moyer focused on radar–communications interference in its Volume 63 published in 1958. Scientific literature on JRC systems remained largely scattered until the 2000s. However, the spectral overlap of centimeter‐wave radars with a number of wireless systems at the 3.5 GHz frequency band led to the 2012 U.S. President's Council of Advisors on Science and Technology (PCAST) report on spectrum sharing. Thereafter, changes in regulation for this band became a driver for spectrum‐sharing research programs of multiple agencies including the Defense Advanced Research Projects‐Agency (DARPA) and National Science Foundation (NSF). Today, it is the higher end of the RF spectrum–millimeter‐wave and terahertz band–that requires concerted efforts for spectrum management. Some recent studies also mention joint visible light communications (VLC) and visible light positioning (VLP). Conceptual articles have also been reported on joint quantum communications and quantum sensing.
At the time of the publication of this book, journals/conferences sponsored by the following 15 IEEE societies have published JRC studies: IEEE Aerospace and Electronic Systems Society, IEEE Antennas & Propagation Society, IEEE Circuits and Systems Society, IEEE Communications Society, IEEE Computer Society, IEEE Control Systems Society, IEEE Engineering in Medicine and Biology Society, IEEE Geoscience and Remote Sensing Society, IEEE Information Theory Society, IEEE Instrumentation & Measurement Society, IEEE Microwave Theory and Techniques Society, IEEE Photonics Society, IEEE Signal Processing Society, IEEE Solid‐State Circuits Society, and IEEE Vehicular Technology Society. In addition, spectrum sharing and/or JRC working groups and task forces have been reported from various IEEE societies, ITU, International Union of Radio Science (URSI), and American Meteorological Society (AMS).
In the context of overwhelmingly fast‐paced developments in JRC, the goal of this book is twofold:
– to provide a list of references to JRC researchers and engineers, helping them to find information required for their current research, and
– to serve as a reference text in an advanced graduate level course on JRC.
The book consists of three parts: fundamental limits and background; physical layer signal processing; networking and hardware implementation.
Part I: Fundamental Limits and Background
We begin with a historical context, nomenclature, classification, and the principles of JRC systems through the first five chapters.
Chapter 1: A Signal Processing Outlook Toward Joint Radar‐Communications
This chapter provides an overview of the JRC origins and subsequent developments. It details various signal processing (SP) techniques employed to achieve JRC systems by exploiting different degrees of freedom at transmitter and receiver as well as information on the channel state and target scenarios. The chapter summarizes various current JRC topologies and state‐of‐the‐art solutions. It describes in detail the generic technical issues related to both transmit and receive signal processing, with examples of each prevailing design topology.
Chapter 2: Principles of Radar‐Centric Dual‐Function Radar‐Communication Systems
This chapter provides an overview of existing radar‐centric DFRC techniques and discusses their potential and future directions. It focuses on radar as an incumbent service defining the system resources while the communications functionality is seen as an added service carried out using the same system resources under the conditions of minimal overhead and changes to radar parameters. This dual functionality is enabled through multiple signal and system design strategies, each of which is clearly elucidated. The techniques presented leverage the developments in concepts, algorithms, design, and implementation of radar‐centric waveforms, and the chapter offers an ideal platform for the reader on the technical details of DFRC.
Chapter 3: Interference, Clutter, and Jamming Suppression in Joint Radar–Communications Systems – Coordinated and Uncoordinated Designs
Expanding the scope of the DFRC design, this chapter introduces interference due to the coexistence of radar and communications systems and explores different interference suppression methods. It starts with the joint design of transmission and reception strategies in MIMO radar and MIMO communications systems wherein information exchange is enabled for coordinated interference suppression. Subsequently, an uncoordinated approach is pursued whereby interference suppression is integrated into the processing method used for the system's primary function. The chapter leverages constrained maximization techniques including iterative optimization, sparse modeling, and model‐based deep learning architecture.
Chapter 4: Beamforming and Interference Management in Joint Radar–Communications Systems
This chapter continues the discussion of interference management and focuses on beamforming, multicarrier waveform design, and interference management using precoder and decoder designs in joint radar and communications systems. Cooperative settings where information about the state of the radio spectrum and awareness of the environment can be exchanged among the users and subsystems for mutual benefit are considered. A variety of precoding and decoding methods that allow for managing interference to avoid significant deterioration of sensing or communications performance are studied. Orthogonal multicarrier designs facilitating simultaneous communications and sensing and efficient Doppler estimation are also designed.
Chapter 5: Information Theoretic Aspects of Joint Sensing and Communications
An understanding of the limits of joint sensing and communications needs to be understood to set a path for research and offer a benchmark for design. This chapter precisely addresses this aspect in a simplified yet representative point‐to‐point communications scenario. The joint system is abstracted as a transmitter, equipped with onboard sensors, wishing to convey a message to its intended receiver and simultaneously estimate the corresponding parameters of interest from the backscattered signals modeled as generalized feedback. The chapter brings out the relation between the information theoretic quantities of capacity and distortion measures for the two systems and illustrates the findings with examples.
Part II: Physical‐Layer Signal Processing
The earlier chapters dealt with the presentation of the background to JRC systems and brought out some interesting fundamental trade‐offs. The subsequent four chapters deal with the various types of physical layer signal processing for JRC, largely focusing on enabling spectrum sharing.
Chapter 6: Radar‐Aided Millimeter Wave Communication
This chapter takes a different look at the integration of the two systems, radar and communications. It presents the idea of radar‐aided MIMO communication, motivated by the need to reduce the high overhead associated with the set‐up and maintenance of millimeter wave links involving large arrays. The chapter exploits radar sensors mounted at the base station/access points or the user terminals as a source of out‐of‐band information for overhead reduction. It presents some of the initial works on strategies that leverage diverse information extracted from radar sensors to significantly reduce MIMO communications overhead, both in line‐of‐sight and non‐line‐of‐sight scenarios. Classical optimization and learning‐based approaches are presented in this context.
Chapter 7: Design of Constant‐Envelope Radar Signals Under Multiple Spectral Constraints
Spectrum sharing, in addition to hardware reuse, has been a key motivating factor for joint radar and communications systems. This aspect is elucidated in this chapter, which focuses on the synthesis of radar waveforms optimizing surveillance system performance while guaranteeing coexistence with the surrounding radio frequency emitters, via multiple spectral compatibility constraints. The coexistence with the communications system is controlled by guaranteeing a certain quality of service explicitly and through interference energy management implicitly. The spectrum‐sharing problem is then cast in an optimization framework and solved using an iterative optimization procedure with established convergence and linear complexity in certain design parameters.
Chapter 8: Spectrum Sharing Between MIMO Radar and MIMO Communication Systems
Continuing on the topic of spectrum sharing, this chapter starts with a detailed canvas of the current spectrum‐sharing scenario and then considers a sparse‐sensing‐based MIMO radar architecture and a MIMO communications system, operating in the vicinity of each other. In this cooperative setting, the joint design in the spectrum‐sharing paradigm is formulated as a constraint optimization problem where the sparse sensing notion is embedded in the radar system design. In particular, suitable precoded random unitary waveforms are transmitted and a sub‐sampling strategy is employed at the receiver. These novel elements have been jointly designed by the control center to maximize the radar performance while meeting certain constraints for the communications system.
Chapter 9: Performance and Design for Cooperative MIMO Radar and MIMO Communications
This chapter brings out the necessity and advantages of cooperation in coexisting radar and communications systems. Such a coexisting system offers fewer constraints and more design freedom to enhance performance, unlike a jointly designed system. In this context, a hybrid active–passive radar employs target returns contributed from both the radar and communications transmitters for target localization and localization. In a similar vein, signals reflected from the radar target are exploited along with those received directly from communications transmitters. The chapter derives the performance metrics and mechanisms to optimize the cooperative system fully using the available observations in the system to surpass the performance of the existing systems.
Part III: Networking and Hardware Implementations
Having considered the physical layer contributions, the subsequent chapters deal with higher layer aspects including classical resource allocation in the new paradigm as well as the emerging topics of secrecy and privacy. To establish the credibility of the research, hardware demonstration is essential, and Chapter 12 brings out the efforts in that direction.
Chapter 10: Frequency‐Hopping MIMO Radar‐based Data Communications
This chapter considers a different degree of freedom for a JRC system that exploits a frequency‐hopping MIMO radar to achieve a reasonably high‐speed and long‐distance data communications link. Several strategies to embed the information to be transmitted in the frequency‐hopping system are detailed along with the appropriate information demodulation techniques. This has been encapsulated in a comprehensive presentation of the system and signal model. Starting with ideal assumptions, the chapter then progresses to detail some of the recent breakthroughs in timing offset and channel estimations as well as the challenges. The chapter also highlights signaling strategies for higher data rates, the impact of the multipath channels, and security issues.
Chapter 11: Optimized Resource Allocation for Joint Radar‐Communications
An aspect of joint design involves the allocation of available resources to the two systems so as to optimize the performance of each of them. In this context, this chapter considers resource allocation, a key concept in enabling three different JRC architectures. A single transmit antenna‐based system using OFDM jointly optimizes radar and communications by subcarrier and power allocation. Optimal antenna selection and power is considered for multi‐antenna systems, and the problem of optimal power allocation in distributed systems is also addressed based on the target localization performance and the communications capacity. The chapter opens up a resource allocation over a broader range of constraints, scenarios, and emerging joint sensing and communications paradigms.
Chapter 12: Emerging Prototyping Activities in Joint Radar‐Communications
The previous chapters have discussed the canvas of JRC, highlighting the key approaches of radar‐centric, communications‐centric, and dual‐function radar–communications systems. A hardware validation of these techniques would lend credence to the results while enabling their embrace by industry. To this end, this chapter presents some of the prototyping initiatives that address some salient aspects of JRC systems. The chapter describes some existing prototypes to highlight the challenges in the design and performance of JRC. In particular, a coexistence prototype is detailed and two other developments are summarized.
Chapter 13: Secrecy Rate Maximization for Intelligent Reflective Surface‐Assisted MIMO Communication Radar
The intelligent reflective surface is an emerging technology for enhancing coverage and performance of largely communications systems, but of late, also radar systems. This chapter investigates the joint transmitter beampattern and phase shifts of the intelligent surfaces in the context of a MIMO joint radar and communications system with an eavesdropping target. It brings in the concept of secrecy rate to JRC and shows that the use of reflective surfaces enhances the secrecy rate performance compared to ordinary MIMO radar. In particular, secrecy rate maximization at the legitimate user and the transmit power minimization lead to non‐convex problems, which are then handled in the chapter using iterative optimization to derive two beamforming vectors, one to detect the target and the other to serve legitimate users.
Chapter 14: Privacy in Spectrum Sharing Systems with Applications to Communications and Radar
The concluding chapter of the book looks at another emerging topic of significant ramifications given the awareness about privacy. Since the two systems share the spectrum, which invariably involves information exchange, concerns of user privacy arise. This chapter explores techniques for the design and operation of radar and communications systems in shared spectrum environments, including analytical methods, models, and optimization to achieve performance and user privacy objectives. In particular, the chapter explores the characteristics of the privacy–performance trade‐off with privacy evaluated in terms of inference attacks. The general evaluation framework enables the formulation of optimal privacy preservation strategies to offer a benchmark for future system design.
The book concludes with an epilogue that captures the future directions in this field. In summary, this book highlights some pioneering leaps in a very wide spectrum of JRC technologies. The plethora of JRC applications, their possible variants, and their relative benefits make it difficult to conclude which JRC design and algorithm will always be the best. The only foregone conclusion is that the JRC community will continue to complement the strengths of each technique and customize them as per application‐specific requirements. We hope that this book helps demonstrate this rapidly evolving signal processing technology.
We thank all contributing authors for submitting their high‐quality contributions. We sincerely acknowledge the support and help from all the reviewers for their timely and comprehensive evaluations of the manuscripts that have improved the quality of this book.
Finally, we are grateful to the IEEE Press Editorial Board and the Wiley staff members Ms. Aileen Storry and Kimberly Monroe‐Hill for their support, feedback, and guidance.
Kumar Vijay Mishra
Adelphi, Maryland, USA
M. R. Bhavani Shankar
Luxembourg
Björn Ottersten
Stockholm, Sweden
A. Lee Swindlehurst
Irvine, California, USA
Acknowledgments
K. V. M. acknowledges support from the National Academies of Sciences, Engineering, and Medicine via Army Research Laboratory Harry Diamond Distinguished Fellowship.
B. O. and M. R. B. S. acknowledge the funding under the European Research Council Advanced Grant, Actively Enhanced Cognition based Framework for Design of Complex Systems (AGNOSTIC), bearing the reference
EC/H2020/ERC2016ADG/742648/AGNOSTIC.
Part IFundamental Limits and Background
1A Signal Processing Outlook Toward Joint Radar‐Communications
Kumar Vijay Mishra1, M. R. Bhavani Shankar2, Björn Ottersten3, and A. Lee Swindlehurst4
1United States CCDC Army Research Laboratory, Adelphi, MD, USA
2Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg
3KTH Royal Institute of Technology, Stockholm, Sweden
4University of California, Irvine, CA, USA
1.1 Introduction
In recent years, sensing systems (radar, lidar, or sonar) that share the spectrum with wireless communications (radio‐frequency/RF, optical, or acoustical) and still operate without any significant performance losses have captured significant research interest [Paul et al., 2017; Hassanien et al., 2017]. The interest in such spectrum‐sharing systems is largely because the spectrum required by the wireless media is a scarce resource, while performance of both communications and remote sensing systems improves by exploiting a wider spectrum.
Several portions of frequency bands – from Very High Frequency (VHF) to Terahertz (THF) – are allocated exclusively for different radar applications [Cohen et al., 2018]. Although a large fraction of these bands remains underutilized, radars need to maintain constant access to these bands for target sensing and detection as well as obtain more spectrum to accomplish missions such as secondary surveillance, multi‐function integrated RF operations, communications‐enabled autonomous driving and cognitive capabilities. On the other hand, the wireless industry's demand for spectrum continues to increase for providing new services and accommodating a massive number of users with a high data rate requirement. The present spectrum is used very inefficiently due to its highly fragmented allocation. Emerging wireless systems such as commercial Long Term‐Evolution (LTE) communications technology, fifth‐generation (5G), Wi‐Fi, Internet‐of‐Things (IoT), and Citizens Broadband Radio Services (CBRS) already cause spectral interference to legacy military, weather, astronomy, and aircraft surveillance radars (ASR) [Paul et al., 2017; Cohen et al., 2018]. Similarly, radar signals in adjacent bands leak to spectrum allocated for communications and deteriorate the service quality. Therefore, it is essential and beneficial for radar and communications to develop strategies to simultaneously and opportunistically operate in the same spectral bands in a mutually beneficial manner.
At the lower end of the spectrum in the VHF (30–300 MHz), Ultra High Frequency (UHF) (300–1000 MHz), and L‐bands (1–2 GHz), radar systems such as FOliage PENetration (FOPEN) radar, astronomy/ionosphere radars, and Air Route Surveillance Radar (ARSR) have been encountering and managing interference from the broadcast and TV stations for decades now. The spectral congestion in centimeter‐wave (cmWave) bands (S‐, C‐, X‐, Ku‐, and K‐) arose later, primarily due to LTE waveforms, e.g. 802.11b/g/n (2.4 GHz) Wide‐band Code Division Multiplexing Access (WCDMA), WiMAX LTE, LTE Global System for Mobile (GSM) communication, Enhanced Data rates for GSM Evolution (EDGE), 802.11a/ac Very High Throughput (VHT) wireless LAN (WLAN), and commercial flight communications that now share spectrum with radars such as ASR, Terminal Weather Doppler Radar (TDWR) network, and other weather radars.
The spectral overlap of cmWave radars with a number of wireless systems at the 3.5 GHz frequency band led to the 2012 U. S. President's Council of Advisors on Science and Technology (PCAST) report on spectrum‐sharing [2012], and changes in regulation for this band became a driver for spectrum‐sharing research programs of multiple agencies [Cohen et al., 2018].
Today, it is the higher end of the RF spectrum, i.e., the millimeter‐wave (mmWave), formally defined with the frequency range 30–300 GHz, that requires concerted efforts for spectrum management because its technologies are in an early development stage. Increasingly, the mmWave systems [Rappaport et al., 2015] are the preferred technology for near‐field communications since they provide transmission bandwidth that is several GHz wide and currently unlicensed. This enables applications that require huge data rates such as 5G wireless backhaul, uncompressed high‐definition (HD) video, in‐room gaming, intra‐large‐vehicle communications, inter‐vehicular communications, indoor positioning systems, and IoT‐enabled wearable technologies [Daniels and Heath Jr, 2007]. There has also been a spurt of novel sensing systems in the mmWave band. Although these devices typically have short ranges because of heavy attenuation by physical barriers, weather, and atmospheric absorption, they provide high‐range resolution resulting from the wide bandwidth. Typical mmWave radar applications include autonomous vehicles [Dokhanchi et al., 2019a], gesture recognition [Lien et al., 2016], cloud observation [Mishra et al., 2018], RF identification [Decarli et al., 2014], indoor localization [Mishra and Eldar, 2017b], and health monitoring [Fortino et al., 2012].
A recent rise of both radar and communications applications at terahertz (THz) band has also led to the development of integration of radar sensing and communications functionalities at these frequencies [Elbir et al., 2021]. The precise definition of THz band varies among different community members. Recent works in wireless communications generally define this band in the range 0.03–10 THz with an obvious overlap with the conventional mmWave frequencies. For the radar, microwave, and remote sensing engineers, THz band starts at the upper‐mmWave limit of 100 GHz, and, in particular, low‐THz term is used for the range 0.1–1 THz. In optics, on the other hand, THz spectrum is defined to end at 10 THz, beyond which frequencies are considered far‐infrared. The Terahertz Technology and Applications Committee of the IEEE Microwave Theory and Techniques Society (MTT‐S) focuses on the 0.3–3 THz range, while the IEEE Transactions on Terahertz Science and Technology Journal targets 0.3–10 THz.
Table 1.1 summarizes some of the co‐existing communications services across various IEEE radar bands. In this chapter, we provide an overview of signal processing techniques and aspects of spectrum‐sharing across different bands.
Table 1.1 Co‐existing radar systems and communications services at different IEEE radar bands
IEEE radar band
VHF/UHF [30 MHz–1 GHz]
L [1–2 GHz]
S [2–4 GHz]
C [4–8 GHz]
X [8–12 GHz]
Ku, K, Ka, V, W, THz [12–300 GHz]
Example radar systems
FOPEN
ARSR
ASR, Next‐Generation Weather Radar (NEXRAD)
TDWR
Mobile weather radars
Automotive radars, cloud radars
Co‐existing communications
TV/broadcast/802.11ah/f
WiMAX, Joint Tactical Information Distribution System (JTIDS)
LTE
802.11a/ac
LTE
802.11ad, mmWave and THz communications
1.2 Policy and Licensing Issues
In the United States, a 75 MHz bandwidth within the 5.9 GHz band (specifically ranging from 5.850 to 5.925 GHz) has been exclusively assigned to intelligent transportation systems (ITSs) and car safety purposes for the past two decades, employing the dedicated short‐range communications (DSRCs) technology. However, due to the lack of advancements in the DSRC band and the exponential growth of Wi‐Fi technology, the Federal Communications Commission (FCC) recently designated the lower 45 MHz (5.850–5.895 GHz) for unlicensed applications, such as Wi‐Fi. Consequently, only the upper 30 MHz spectrum (5.895–5.925 GHz band) is presently allocated for dedicated usage by ITS technologies. While the FCC has reserved 30 MHz of spectrum for critical safety services, advanced connected vehicle services will necessitate additional spectrum resources, particularly for data‐intensive applications like augmented reality. Fortunately, the FCC has made available the 5 GHz unlicensed frequency band (∼500 MHz) for 3GPP technologies, which can be utilized by cellular vehicle‐to‐everything (C‐V2X) communications.
DSRC is an ad hoc communication system that operates independently of network infrastructure. Due to its widespread availability, many automobile manufacturers seeking to adopt V2X communications have favored the IEEE 802.11p standard, which serves as the foundation for DSRC. However, the implementation of 802.11p (and its successor, 802.11bd) necessitates the installation of numerous new access points (APs) and gateways, resulting in extended deployment time and increased costs. Given the absence of a clear business model, it is challenging to find an operator willing to bear the expenses associated with deploying numerous new APs based on freely available 802.11p/bd technologies. Consequently, the FCC has endorsed and prioritized C‐V2X technologies based on the 3GPP standards, utilizing the 5.9 GHz frequency band, to pave the way for future connected vehicular systems. This includes requirements for over‐the‐air (OTA) software updates and the establishment of a car‐to‐cloud ecosystem.
The CBRS band serves as an intriguing case study due to the unequal access priority and the necessity of accurately detecting incumbents with 99% accuracy, even in the presence of lower‐priority transmissions. Presently, tier 1 naval radar is safeguarded by a threshold limit on the combined power of noise and transmissions within the designated surrounding whisper zones. This necessitates a reduction in transmission power levels for LTE/5G priority access license (PAL) grantees, which consequently impacts consumer devices. The requirement for low power usage by PAL users becomes further complicated in the context of mobile radar, as the whisper zones may dynamically shift around the radar's moving location.
