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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
JOINT COMMUNICATIONS AND SENSING Authoritative resource systematically introducing JCAS technologies and providing valuable information and knowledge to researchers and engineers Based on over six years of dedicated research on joint communications and sensing (JCAS) by the authors, their collaborators, and students, Joint Communications and Sensing is the first book to comprehensively cover the subject of JCAS, which is expected to deliver huge cost and energy savings, and therefore has become a hallmark of future 6G and next generation radar technologies. The book has three parts. Part I presents the basic JCAS concepts and applications and the basic signal processing algorithms to support JCAS. Part II covers communications-centric JCAS designs that describe how sensing can be integrated into communications networks such as 5G and 6G. Part III presents ways to integrate communications in various radar sensing technologies and platforms. Specific sample topics covered in Joint Communications and Sensing include: * Three categories of JCAS systems, potential sensing applications of JCAS, signal processing fundamentals, and channel models for communications and radar * Frameworks for perceptive mobile networks (PMNs), system modifications to enable PMN sensing, and PMN system issues * Orthogonal time-frequency space waveform-based JCAS for IoT, including signal models, echo pre-processing, and target parameter estimation Joint Communications and Sensing provides valuable information and knowledge to researchers and engineers in the communications and radar sensing communities and industries, enabling them to upskill and prepare for JCAS technology research and development. The text is of particular interest to engineers in the wireless communications industry who are pursuing new capabilities in 6G.
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Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Title Page
Copyright
About the Authors
Acknowledgments
Preface
Acronyms
Part I: Fundamentals of Joint Communications and Sensing (JCAS)
1 Introduction to Joint Communications and Sensing (JCAS)
1.1 Background
1.2 Three Categories of JCAS Systems
1.3 Potential Sensing Applications of JCAS
1.4 Book Organization
References
2 Signal Processing Fundamentals for JCAS
2.1 Channel Model for Communications and Radar
2.2 Basic Communication Signals and Systems
2.3 MIMO Radar Signals and Systems
2.4 Basic Signal Processing for Radar Sensing
2.5 Signal Processing Basics for Communication-Centric JCAS
2.6 Signal Processing Basics for DFRC
2.7 Conclusions
References
Chapter 3: Efficient Parameter Estimation
3.1
Q
-Shifted Estimator (QSE)
3.2 Refined QSE (QSEr)
3.3 Padé Approximation-Enabled Estimator
3.4 Conclusions
References
Note
Part II: Communication-Centric JCAS
4 Perceptive Mobile Network (PMN)
4.1 Framework for PMN
4.2 System Modifications to Enable Sensing
4.3 System Issues
4.4 Conclusions
References
5 Integrating Low-Complexity and Flexible Sensing into Communication Systems: A Unified Sensing Framework
5.1 Problem Statement and Signal Model
5.2 A Low-Complexity Sensing Framework
5.3 Performance Analysis
5.4 Simulation Results
5.5 Conclusions
References
Notes
6 Sensing Framework Optimization
6.1 Echo Preprocessing
6.2 Target Parameter Estimation
6.3 Optimizing Parameters of Sensing Methods
6.4 Simulation Results
6.5 Conclusions
References
Notes
Part III: Radar-Centric Joint Communications and Sensing
7 FH-MIMO Dual-Function Radar-Based Communications: Single-Antenna Receiver
7.1 Problem Statement
7.2 Waveform Design for FH-MIMO DFRC
7.3 Estimating Timing Offset
7.4 Estimating Channel Response
7.5 Using Estimations in Data Communications
7.6 Extensions to Multipath Cases
7.7 Simulation Results
7.8 Conclusions
References
Notes
8 Frequency-Hopping MIMO Radar-Based Communications with Multiantenna Receiver
8.1 Signal Model
8.2 The DFRC Signal Mode
8.3 A Multiantenna Receiving Scheme
8.4 Performance Analysis
8.5 Simulations
8.6 Conclusions
References
Note
Chapter 9: Integrating Secure Communications into Frequency Hopping MIMO Radar with Improved Data Rates
9.1 Signal Models and Overall Design
9.2 Elementwise Phase Compensation
9.3 Random Sign Reversal
9.4 Simulation Results
9.5 Conclusions
References
Note
A: Proofs, Analyses, and Derivations
A.1 Proof of Lemma 5.1
A.2 Proof of Lemma 5.2
A.3 Proof of Lemma 5.3
A.4 Proof of Proposition 5.1
A.5 Proof of Proposition 5.2
A.6 Proof of Proposition 6.1
A.7 Deriving the Powers of the Four Terms of X̃
n
[
l
] Given in (6.33)
A.8 Proof of Proposition 6.2
A.9 Proof of Proposition 6.3
A.10 Deriving (9.31)
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Comparison of communications and sensing (C&S) with separated wave...
Table 1.2 Comparison among radar, communications, and JCAS.
Table 1.3 Differences of JCAS–Wi-Fi with respect to JCAS-Mobile (PMN).
Table 1.4 Summary of information embedding methods in radar-centric DFRC sys...
Table 1.5 Key research problems in three types of JCAS systems and the assoc...
Table 1.6 Key research problems in three types of JCAS systems and the assoc...
Table 1.7 Potential sensing applications of PMN.
Chapter 3
Table 3.1 Marker and line style definitions in simulation results.
Chapter 4
Table 4.1 Comparison of three types of sensing operations.
Table 4.2 A summary of the properties of the signals that can be used for se...
Table 4.3 Comparison of options of enabling sensing in mobile networks, in t...
Table 4.4 Classification of waveform optimization techniques in PMN.
Table 4.5 Classification of antenna array design techniques in PMN.
Table 4.6 Classification and comparison of Sensing Parameter Estimation Algo...
Chapter 5
Table 5.1 Simulation parameters
Chapter 6
Table 6.1 Values of
Table 6.2 Values of
Table 6.3 Overall parameter estimation.
Table 6.4 Estimating
(or
).
Table 6.5 Simulation parameters.
Table 6.6 Simulation parameters.
Chapter 9
Table 9.1 Maximum achievable rates (MARs) of different radar-based DFRC desi...
Table 9.2 Different FH sequences.
Table 9.3 Combinations of
, where the frequency is in MHz.
List of Illustrations
Chapter 1
Figure 1.1 A classification of the three categories of JCAS systems.
Figure 1.2 Illustration of basic pulse and continuous-wave radar, communicat...
Figure 1.3 Applications and use cases of PMN, with integrated communications...
Chapter 2
Figure 2.1 Block diagram of a MIMO-OFDM communication (a) transmitter and (b...
Figure 2.2 (a) Illustration on the system diagram of an FH-MIMO DFRC; (b) Th...
Figure 2.3 (a) Illustration on the system diagram of an FH-MIMO DFRC; (b) Th...
Figure 2.4 A block diagram of a transceiver showing the components that can ...
Figure 2.5 A simple example showing the packet structure of FH-MIMO DFRC wit...
Chapter 3
Figure 3.1 Illustration of the monotonicity of
w.r.t.
, affected by
.
Figure 3.2 MSE of frequency estimates vs. SNR, where QSE and HAQSE [3], PCQ ...
Figure 3.3 MSE of frequency estimates against SNR.
Figure 3.4 MSE of frequency estimates vs.
, where
and SNR
dB.
Figure 3.5 MSE of frequency estimates vs.
, where SNR
dB.
Figure 3.6 MSE of frequency estimates vs.
, where SNR
dB.
Figure 3.7 MSE of
versus
: (a) is for the first iteration, (b) for the se...
Figure 3.8 Illustration of the ratio between MSE and CRLB to better compare ...
Figure 3.9 MSE performance against
, where
dB, (a) for the first iteratio...
Figure 3.10 MSE performance versus
, where (a) for the first iteration, and...
Figure 3.11 MSE performance versus
, where
dB. The subfigures (a), (b), a...
Chapter 4
Figure 4.1 Illustration of sensing in a PMN with both standalone BS and CRAN...
Figure 4.2 Illustration of the timeslot allocation in a full-duplex TDD JCAS...
Figure 4.3 Simplified TDD transceiver model with a single receiving antenna ...
Figure 4.4 Waveform optimization depicted with respect to packets.
Figure 4.5 Two virtual subarrays with one overlapped antenna are formed: vir...
Figure 4.6 Clutter and two ways of clutter suppression.
Figure 4.7 Illustration of the propagation delay
and timing offset
.
is...
Figure 4.8 Block diagram showing the procedure for pattern recognition.
Figure 4.9 Sensing-assisted communications using multibeam where a fixed sub...
Chapter 5
Figure 5.1 The schematic diagram of a sensing framework, where a sensing rec...
Figure 5.2 SINRs in the RDMs versus
defined in (5.32), where three targets...
Figure 5.3 SINRs in RDMs versus
(the length of VCP), where the values of
Figure 5.4 SINRs in the ratio-based RDM versus
(the number of overlapping ...
Figure 5.5 SINRs in the CCC-based RDM versus
(the number of overlapping sa...
Figure 5.6 Illustration of the detection performance of the sensing framewor...
Figure 5.7 Illustrating
corresponding to the curves in Figure 5.6.
Figure 5.8 Comparing the ratio- and CCC-based RDMs, as given in Figures 5.8(...
Figure 5.9 Comparing the
receiver operating characteristic
(
ROC
) of COS and ...
Figure 5.10 ROC curves under the CCC-based RDMs, corresponding to Figure 5.9...
Figure 5.11 Illustration of the detecting probability versus
corresponding...
Chapter 6
Figure 6.1 Illustrating virtual cyclic prefix (VCP), where
denotes the min...
Figure 6.2 MSE of velocity estimation, denoted by
, vs.
, i.e. the SNR of
Figure 6.3 MSE of
vs.
, where
is taken for the method presented in this...
Figure 6.4 Illustrating the impact of
on the powers of signal and other co...
Figure 6.5 MSE of parameter estimations vs.
, where the mean is calculated ...
Figure 6.6 MSE of parameter estimations vs.
, where
and the mean is calcu...
Chapter 7
Figure 7.1 (a) Illustration on the system diagram of an FH-MIMO DFRC; (b) Th...
Figure 7.2 The impact of using two identical hops on the
range ambiguity fun
...
Figure 7.3 (a) The hopping frequencies of a conventional FH-MIMO radar wavef...
Figure 7.4 (a) MSE of
estimation against
, where “intf.” is short for int...
Figure 7.5 MSE of
estimation against
, where the
estimations from Figur...
Figure 7.6 MSE of
against
, where the
estimations in Figure 7.5 are use...
Figure 7.7 Achievable data rate vs. communication SNR, where AR0 is obtained...
Figure 7.8 SER vs. Eb/N0, where Eb/N0 is the energy per bit to noise power d...
Chapter 8
Figure 8.1 (a) Illustration of the system diagram of an FH-MIMO DFRC; (b) th...
Figure 8.2 Illustration of signal cancelation. (a) The solid curve is for
...
Figure 8.3 MSE of
obtained in (8.14) and
given in (8.24).
Figure 8.4 Communication performance of FHCS-based FH-MIMO DFRC against
, w...
Figure 8.5 Communication performance of BPSK-based FH-MIMO DFRC, with
dB a...
Figure 8.6 The same simulation as done in Figure 8.5 with
dB here.
Figure 8.7 Comparing SER between the presented (PP) multiantenna receiving s...
Figure 8.8 SER of FH-MIMO DFRC against
, where, unless otherwise specified,...
Chapter 9
Figure 9.1 System block diagram of an FH-MIMO DFRC, where radar, besides det...
Figure 9.2 SER against
, where
and
are in the region of
dB. The radar...
Figure 9.3 SER against SNR under different values of
.
Figure 9.4 Illustration of the error probability of detecting random sign re...
Figure 9.5 SER against SNR under different values of
, where
. Note that “...
Figure 9.6 SER against spatial angle from
to
with a grid of
, where
,
Figure 9.7 Impact of the presented baseband waveform processing on radar det...
Guide
Cover
Title Page
Copyright
About the Authors
Acknowledgments
Preface
Acronyms
Table of Contents
Begin Reading
A: Proofs, Analyses, and Derivations
Index
End User License Agreement
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IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial BoardSarah Spurgeon, Editor in Chief
Jón Atli BenediktssonAnjan BoseAdam DrobotPeter (Yong) Lian
Andreas MolischSaeid NahavandiJeffrey ReedThomas Robertazzi
Diomidis SpinellisAhmet Murat Tekalp
Joint Communications and Sensing
From Fundamentals to Advanced Techniques
Kai Wu
J. Andrew Zhang
Y. Jay Guo
Global Big Data Technologies CentreUniversity of Technology SydneySydney, Australia
Copyright © 2023 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
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About the Authors
Kai Wu received a BE from Xidian University, Xi'an, China, in 2012, a PhD from Xidian University in 2019, and a PhD from the University of Technology Sydney (UTS), Sydney, Australia, in 2020. His Xidian-PhD won the “Excellent Thesis Award 2019” (a highest national award for PhD in electronic engineering fields) from the Chinese Institute of Electronics. His UTS-PhD was included in the “Chancellor's list 2020” of UTS.
Dr Kai Wu is now a research fellow at the Global Big Data Technologies Centre, UTS. His research focus has been on joint communications and sensing post-PhD. In particular, he has advanced waveform and system designs of the frequency-hopping MIMO radar-based communications, with four IEEE journal papers plus a book chapter published on this topic. Dr Wu has also been developing efficient algorithms to perform high-quality sensing using mainstream and future communication waveforms, such as OFDM and OTFS. One of his recent works established a unified sensing framework employing most, if not all, communication waveforms, which has aroused great interest and attention from academia and industry. Moreover, Dr Wu was also a tutorial speaker on joint communications and sensing in IEEE ICC'21 and WCNC'21.
Dr. J. Andrew Zhang received the BSc degree from Xi'an JiaoTong University, China, in 1996, the MSc degree from Nanjing University of Posts and Telecommunications, China, in 1999, and the PhD degree from the Australian National University, in 2004.
Currently, Dr. Zhang is an associate professor in the School of Electrical and Data Engineering, University of Technology Sydney, Australia. He was a researcher with Data61, CSIRO, Australia, from 2010 to 2016, the Networked Systems, NICTA, Australia, from 2004 to 2010, and ZTE Corp., Nanjing, China, from 1999 to 2001. Dr. Zhang's research interests are in the area of signal processing for wireless communications and sensing. He has published more than 250 papers in leading international journals and conference proceedings, and has won five best paper awards. He is a recipient of CSIRO Chairman's Medal and the Australian Engineering Innovation Award in 2012 for exceptional research achievements in multigigabit wireless communications.
Prof. Zhang is one of the leading researchers in joint communication and radar/radio sensing (JCAS) technologies. He initiated the concept of perceptive mobile network, by defining its system framework and demonstrating its feasibility in a set of papers back to 2017. Prof. Zhang co-organized multiple IEEE workshops on JCAS and served as a guest editor for multiple JCAS special issues on leading journals. He has also delivered JCAS conference tutorials in WCNC 2021, ICC 2021, and ICC2022, and offered numerous keynotes and invited talks. He is serving as the publication cochair of ISAC-ETI and the editor-in-chief of its official publications, ISAC-Focus. In addition to academic research, Prof. Zhang is also working with the industry to make JCAS a reality and has developed two proof-of-concept demonstrators. He has led the completion of four industrial projects of applying JCAS technologies in cellular, Wi-Fi, and UAV communication networks and has attracted more than 4 million dollars of research fund in this area.
Y. Jay Guo is a distinguished professor and the founding director of Global Big Data Technologies Centre (GBDTC) at the University of Technology Sydney (UTS), Australia. He is also the founding technical director of the New South Wales Connectivity Innovation Network. Prior to joining UTS in 2014, he served as a research director in CSIRO for over nine years. Before joining CSIRO, he held various senior technology leadership positions in Fujitsu, Siemens, and NEC in the United Kingdom. His research interests include antennas, mm-wave, and THz communications and sensing systems as well as big data technologies. He has published six books and over 600 research papers including over 300 IEEE journal papers, and he holds 26 international patents. His main technical contributions are in the fields of Fresnel antennas, reconfigurable antennas, hybrid antenna arrays and, most recently, analogue multibeam antennas, and joint communications and sensing (JCAS) systems for 6G.
Prof Guo is a fellow of the Australian Academy of Engineering and Technology and a fellow of IEEE. He was a member of the College of Experts of Australian Research Council (ARC, 2016–2018). He has won a number of the most prestigious Australian national awards including the Engineering Excellence Awards (2007, 2012) and CSIRO Chairman's Medal (2007, 2012). He was named one of the most influential engineers in Australia in 2014 and 2015, and one of the top researchers across all fields in Australia in 2020 and 2021, respectively. He and his students have won numerous best paper awards.
Acknowledgments
We would like to thank our families, colleagues, students, and collaborators for their sacrifices and contributions made to our research. We are indebted to our industrial and government sponsors, especially the Australian Research Council (ARC), for the generous funding we have received.
Preface
Wireless communications and radar are traditionally two different industries, academic disciplines, and research communities. Motivated by spectrum and hardware sharing, and energy saving, there has been a rapidly increasing market pull and technology push to integrate the two systems into one for many practical applications. This makes a lot of sense as both of these systems require radio spectrum and employ similar hardware such as antennas, radio frequency (RF), and digital circuits. From the point of signal processing, they both need filtering, Fourier transforms, and, most recently, machine learning.
Early efforts in this endeavor were focused on cognitive radio, with the aim to share resources and reconfigure the system to deliver the functionality required on the fly and manage mutual interference between the two coexisting systems when they arise. However, the current trends in research and industrial development are to design new systems with both functions optimized jointly, thereby maximizing the spectrum and hardware sharing and energy savings while delivering the best performance trade-off. As radar technologies and applications have been expanded and diversified to realize more general sensing, the new dual-function systems are now widely referred to as joint communications and sensing (JCAS) systems, a.k.a., integrated sensing and communications (ISAC). Great examples of JCAS systems are beyond 5G and 6G communication networks that will extend the communication-only designs in previous generation systems to include wireless sensing capabilities. Just imagine: if we can leverage the expected universal wireless communication infrastructure to conduct wide-area and pervasive sensing, we would have unprecedented availability of real-time data to manage our environment, our cities, and industrial activities much more effectively yet with marginal extra cost. Even for this reason alone, we can easily see the huge potential of JCAS and be excited to participate in this technology innovation.
This book has two objectives. First, we intend to provide the fundamentals of basic architectures, applications, and signal processing techniques of JCAS. Second, we aim to present a range of advanced techniques for designing high-performance JCAS systems, including both communication-centric and radar-centric systems. The two systems have communications and sensing as primary functions, respectively. We sincerely hope that the book will prove valuable to both engineers working in wireless communications and sensing industries and graduate students and academic researchers.
Kai Wu, J. Andrew Zhang, and Y. Jay Guo
Acronyms
AoA
angle of arrival
AoD
angle of departure
ANM
atomic norm minimization
BBU
baseband unit
BS
base-station
CACC
cross-antenna cross-correlation
CPI
coherent processing interval
CRAN
cloud radio access network
CS
compressive sensing
CSI
channel state information
C&S
wireless communications and radar/radio sensing
DFRC
dual-function(al) radar communications
DFT
discrete Fourier transform
DMRS
demodulation reference signals
FDD
frequency division duplexing
FMCW
frequency-modulated continuous-wave
GMM
Gaussian mixture model
IFFT
inverse fast Fourier transform
IoT
Internet-of-things
JCAS
joint communications and radio/radar sensing
LFM
linear frequency modulation
LFM-CPM
LFM-continuous phase modulation
LOS
line of sight
MAC
medium access
MI
mutual information
MIMO
multiple-input and multiple-output
MISO
multiple-input and single-output
MMSE
minimum mean-square error
MMV
multi-measurement vector
mmWave
millimeter wave
NLOS
none line of sight
NR
new radio
OFDM
orthogonal frequency-division multiplexing
OFDMA
orthogonal frequency-division multiple access
PHY
physical
PAPR
peak-to-average power ratio
PMN
perceptive mobile network
PDSCH
physical downlink shared channel
PUSCH
physical uplink shared channel
PRB
physical resource-block
RFID
radio frequency identification
RIP
restricted isometry property
RIS
reconfigurable intelligent surface
RMA
recursive moving averaging
RMSE
root mean square error
RRU
remote radio unit
Rx
receiver
SC
single carrier
SDMA
spatial division multiple access
SISO
single input single output
SRS
sounding reference signals
SSB
synchronization signal and broadcast blocks
TDD
time-division duplexing
Tx
transmitter
UE
user equipment
ULA
uniform linear array
V2V
vehicle to vehicle
Part IFundamentals of Joint Communications and Sensing (JCAS)
1Introduction to Joint Communications and Sensing (JCAS)
1.1 Background
Wireless communications and radar sensing have been advancing in parallel for decades. Numerous new system architectures and algorithms have been developed in the names of new generation wireless communications systems and modern radar, respectively. However, despite the fact that they share many commonalities in terms of signal processing algorithms, devices and, to certain extent, system architecture, there have been very limited intersections between the designs and the deployment of the two systems. Chiefly motivated by spectrum and cost sharing and energy saving, we are witnessing a rapidly growing interest in the coexistence, cooperation, and, most importantly, joint design of the two systems recently [1–5].
The coexistence of communication and radar systems is not new, and the issue has been extensively studied in the past decade. The focus was on developing efficient interference management techniques in order for the two individually deployed systems to operate simultaneously without interfering with each other [6]. In this setup, radar and communication systems may be colocated and or spatially separated, and they may transmit two different signals overlapped in time and/or frequency domains. They can operate simultaneously by sharing the same resources cooperatively, with a goal of minimizing interference to each other. Great efforts have been devoted to mutual interference cancelation in this case, using, for example, beamforming design, cooperative spectrum sharing, opportunistic primary–secondary spectrum sharing, and dynamic coexistence. However, effective interference cancelation typically has stringent requirements on the mobility of nodes and information exchange between them. The spectral efficiency improvement is hence limited in such schemes.
Since the interference in coexisting systems is caused by transmitting two separate signals, it is natural to ask whether it is possible to use one single transmitted signal for both communications and radar sensing. Radar systems typically use specially designed waveforms such as short pulses and chirps, which enable high-power radiation and simple receiver processing. However, these waveforms are not necessarily required for radar sensing. Passive radar or passive sensing is a good example of exploring diverse radio signals for sensing [7, 8]. In principle, the objects to be sensed or detected can be illuminated by any radio signals of sufficient power, such as TV signals, Wi-Fi signals, and mobile (cellular) signals. This is because the propagation of radio signals is always affected by the static and dynamic environments such as transceiver movement, surrounding objects' movement and profile variation, and even weather changes. Hence, the environmental information is embedded in the received radio signals and can be extracted by using passive radar techniques. However, there are two major limitations with passive sensing. First, the clock phases between transmitter and receiver are not synchronized in passive sensing, and there are always unknown and possibly time-varying timing, frequency, and phase offsets between the transmitted and received signals. This leads to timing and therefore ranging ambiguity in the sensing results, as well as causing difficulties in aggregating multiple measurements for joint processing. Second, the sensing receiver may not know the signal structure. As a result, passive sensing lacks the capability of interference suppression, and it cannot separate multiuser signals from different transmitters. Admittedly, the radio signals are usually not optimized for sensing in any way.
The most recent trend is that radar systems are evolving toward more general radio sensing. We prefer the term “radio sensing” to radar due to its generality and comprehensiveness. Radio sensing here refers to retrieving information from received radio signals; this is in contrast to extracting information from the communication data modulated to the signal at the transmitter. It can be achieved through the measurement of sensing parameters related to location and movement, such as time delay, angle-of-arrival (AoA), angle-of-departure (AoD), Doppler frequency, and magnitude of multipath signals, and physical feature parameters such as inherent “radio signature” of devices/objects/activities. The two corresponding processing activities are called sensing parameter estimation and pattern recognition in this book. In this sense, radio sensing refers more to general sensing techniques and applications using radio signals, just like video sensing using video signals. Radio sensing has a diverse range of applications such as object, activity, and event recognition in Internet of Things (IoT), Wi-Fi, and 5G networks. These radio signals are transmitted by an existing infrastructure and are not specifically designed for sensing purpose. The paper [9] presents numerous Wi-Fi sensing applications where, for instance, Wi-Fi signals have been used for people and behavior recognition in indoor environments. In [10], it is shown that other radio signals, such as radio-frequency identification (RFID) and ZigBee, can also be used for activity recognition. These publications demonstrate the strong potential of using low-bandwidth communication signals for radio-sensing applications.
Joint communications and radar/radio sensing (JCAS) [11, 12] is emerging as an attractive solution to integrating communications and sensing into one system. It has also been known under different terms, such as radar-communications (RadCom) [1], joint radar (and) communications (JRC) [3, 13, 14], joint communications (and) radar (JCR) [15], dual-function(al) radar communications (DFRC) [16, 17], and more recently, integrated sensing and communications (ISAC). In a JCAS system, a single transmitted signal for both communications and sensing is jointly designed and employed. The objective for JCAS is that the majority of transmitter modules can be shared by communications and sensing. In such a system, most of the receiver hardware can also be shared, but some receiver baseband signal processing would be different for communications and sensing. By virtue of joint design, JCAS can also potentially overcome the many limitations in passive sensing. These properties make JCAS significantly different from existing spectrum sharing concepts such as cognitive radio, the aforementioned coexisting communication-radar systems, and “integrated” systems using separated waveforms [18] where communications and sensing signals are separated in such resources as time, frequency, and code, despite the two functions may physically be combined in one system. In Table 1.1, we briefly compare the signal formats and key features, advantages, and disadvantages of five types of systems: communications and sensing with separated waveforms, coexisting communications and sensing, passive sensing, cognitive radio, and JCAS.
1.2 Three Categories of JCAS Systems
The initial concept of integrated communications and sensing may be traced back to the 1960s [3] and had been primarily investigated for developing multimode or multifunction military radars. In early days, most of such systems belonged to the type in which communications and sensing use separated waveforms, as detailed in Table 1.1. There has been very limited research on JCAS for civil systems before 2010. In the past 10 years, JCAS has been receiving rapidly growing interest and is being considered as a candidate for next generations of communications, radar, and sensing systems.
Based on the design priority and the underlying signal formats, the current JCAS systems may be classified into the following three categories:
Communication-centric design
: In this class, radio sensing is an add-on to a communication system, where the design priority is on communications. The aim of such a design is to exploit communication waveform to extract sensing information through target echoes. Enhancements to hardware and algorithms are required to support radio sensing. Possible enhancements to communication standards may be introduced to enable better reuse of the communication waveform for radio sensing. In this design, the communication performance can be largely unaffected; however, the sensing performance may be scenario-dependent and difficult-to-optimize.
Table 1.1 Comparison of communications and sensing (C&S) with separated waveforms, coexisting communications and sensing, passive sensing, cognitive radio, and JCAS.
Systems
Signal formats and key features
Advantages
Disadvantages
C&S with separated waveforms (e.g.
[18]
)
– C&S signals are separated in time, frequency, code and/or polarization
– C&S hardware and software are partially shared
– Low mutual interference
– Almost independent design of C&S waveforms
– Low-spectrum efficiency
– Low order of integration
– Complex transmitter hardware
Coexisting C&S (e.g.
[6
,
19]
)
C&S use separated signals but share the same resource
Higher-spectrum efficiency
– Interference is a major issue
– Nodes cooperation and complicated signal processing are typically required
Passive sensing (e.g.
[7
,
8
,
20
,
21]
)
– Received radio signals are used for sensing at a specifically designed sensing receiver, external to the communication system
– No joint signal design at transmitter
– Without requiring any change to existing infrastructure
– Higher-spectrum efficiency
– Require dedicated sensing receiver
– Timing ambiguity
– No waveform optimization
– Noncoherent sensing and limited-sensing capability when signal structure is complicated and unknown, e.g. incapable of separating multiuser signals from different transmitters
Cognitive radio (e.g.
[22]
)
Secondary systems coexist with primary ones by sensing spectrum holes or via interference mitigation
– Improved spectrum efficiency
– Negligible impact on the operation of primary systems
Performance of secondary systems cannot be guaranteed. They also have higher complexities due to requirement for spectrum sensing and potential interference suppression
JCAS (e.g. [
3
,
13
,
17
,
23
,
24
])
A common transmitted signal is jointly designed and used for communications and sensing
– Highest-spectral efficiency
– Fully shared transmitter and largely shared receiver
– Joint design and optimization on waveform, system, and network
– “Coherent sensing”
– Requirement for full-duplex or equivalent capability of a receiver colocating with the transmitter
– Sensing ambiguity when transmitter and receiver are separated without clock synchronization
Radar-centric design
: Conversely, such approaches aim at modulating or introducing information signaling in known radar waveforms. Since the radar signaling remains largely unaltered, a near optimal radar performance can be achieved. The main drawback of such approaches is the limited achievable data rates. If some performance loss can be tolerated by the radar system, better communication data rates could be obtained. Given the high-transmission power of typical radar systems, very long range communications can generally be achieved.
Joint design and optimization
: This class encompasses systems that are jointly designed from the start to offer a tunable trade-off between communications and sensing performance. Such systems may not be limited by any of the existing communication or radar standards and can be optimized by jointly and fairly considering the requirements for both communications and sensing.
Owing to the significant differences between traditional communication and sensing systems, the design problems in these three categories are quite different. In the first two categories, the design and research focus are typically on how to realize the other function based on the signal formats of the primary system, with the principle of not significantly affecting the primary system, though slight modifications and optimizations may be applied to the system and signals. The last category considers the design and optimization of the signal waveform, system, and network architecture, without bias to either communications or sensing, aiming at fulfilling the desired applications only.
Next, we first briefly discuss the major differences between traditional communication and radar signals, which are important for understanding the design philosophy of the three categories of JCAS systems. We then provide a brief review on the recent research progress in each of the categories, referring to the classification of the three categories of JCAS systems in terms of their technical scope, as shown in Figure 1.1.
1.2.1 Major Differences Between Communications and Sensing
Communication and radar signals are originally designed for different objectives and are generally not directly applicable to each other. Radar signals are typically designed to achieve high localization and tracking accuracy and to enable simple sensing parameter estimation. The following properties of radar signals are desired: low peak-to-average-power ratio (PAPR) to enable high-efficiency power amplifier and long-range operation; and a waveform ambiguity function with steep and narrow mainlobes for high resolution. In contrast, communication signals are designed to maximize the information-carrying capabilities and are typically modulated and packet-based. To support diverse devices and meet various quality-of-services requirements, communication signals can have complicated structures, with advanced modulations applied across time, frequency, and spatial domains, and being discontinuous and fragmented over these domains.
Figure 1.1 A classification of the three categories of JCAS systems.
Figure 1.2 presents the simplified transceivers and signal structures of C&S to illustrate their major differences.
Figure 1.2 Illustration of basic pulse and continuous-wave radar, communication systems, and JCAS systems. Tx stands for transmitter; Rx for receiver; PRI for pulse repetition interval; and BPF for bandpass filter.
Conventional radar systems include pulsed and continuous-wave radars [25], as shown in Figure 1.2. In pulsed radar systems, short pulses of large bandwidth are transmitted either individually or in a group, followed by a silent period for receiving the echoes of the pulses. Continuous-wave radars transmit waveforms, such as chirp, continuously, typically scanning over a large range of frequencies. In either system, the waveforms are typically nonmodulated. These waveforms are used in both single-input and single-output (SISO) and multiple-input and multiple-output (MIMO) radar systems, with orthogonal waveforms used in MIMO radars [25].
In most of radar systems, low PAPR is a desired feature for the transmitting signal, which enables the use of high-efficiency power amplifier and long-range operation. The transmitting waveform is also desired to have an ambiguity function with steep and narrow mainlobes, which are the correlation function of the received echo signals and the local template signal. These waveforms are designed to enable low-complexity hardware and signal processing in radar receivers, for estimating key sensing parameters such as delay, Doppler frequency, and angle of arrival. However, these waveforms are not indispensable for estimating these parameters. A pulsed radar receiver typically samples the signal at a high-sampling rate twice of the transmitted pulse bandwidths, or at a relatively lower-sampling rate at the desired resolution of the delay (ranging); while the receiver of a continuous-wave radar, e.g. frequency modulated continuous wave (FMCW) radar, typically samples signals of “beat” frequency at a rate much smaller than the scanning bandwidth, proportional to the desired detection capability of the maximal delay. Here, the beat frequency refers to the difference between the frequencies of the echo signal and the transmitted signal that is used as the input to the local oscillator of the receiver, and contains the range information. Note that the FMCW signal has a unique time–frequency relationship, typically, the frequency () is a linear function of time (), namely, with a nonzero coefficient determined by radar configurations. Since the echo signal is essentially the scale of the transmitted signal with a time delay, the time–frequency relationship can be preserved yet also with a time delay, i.e. with being the delay. Consequently, the frequency conversion at the FMCW receiver cancels in the frequency, yielding a signal with a constant frequency that is solely related to target delay. Due to their special signal form and hardware, radar systems generally cannot support very high-rate communications without significant modifications to the waveforms and/or receiver structure [4].
In contrast, communication signals are designed to maximize the information-carrying capabilities. They are typically modulated, and modulated signals are usually appended with nonmodulated intermittent training signals in a packet, as can be seen from Figure 1.2. To support diverse devices and meet various service requirements, communication signals can be very complicated. For example, they can be discontinuous and fragmented over time and frequency domains, have high PAPR, have complicated signal structures due to advanced modulations applied across time, frequency, and spatial domains. Although being designed without considering the demand for sensing, communication signals can potentially be used for estimating all the key sensing parameters. However, different from conventional channel estimation, which is already implemented in communication receivers, sensing parameter estimation requires extraction of the channel composition rather than channel coefficients only. Such detailed channel composition estimation is largely limited by the hardware capability. The complicated communication signals are very different to conventional radar and demand new sensing algorithms. We note that the detailed information on the signal structure, such as resource allocation for time, frequency, and space, and the transmitted data symbols, can be critical for sensing. For example, the knowledge on signal structure is important for coherent detection. In comparison, most passive radar sensing can only perform noncoherent detection with the unknown signal structure, and hence only limited sensing parameters can be extracted from the received signals with degraded performance [7].
There are also practical limitations in communication systems, such as full-duplex operation and asynchronization between transmitting node and receiving node, which requires new sensing solution to be developed. It is a fundamental challenge to address the potential requirements for full duplex operation of JCAS systems where the transmitter and sensing receivers are colocated. On the one hand, monostatic radar addresses the requirement for full-duplex operation in mainly two approaches, as illustrated in Figure 1.2, which may not be replicable in communication systems. One approach is typically applied in a pulsed radar, by applying a long silent period to receive echo signals, which essentially bypasses full-duplex operation and make the radar work in a time-division duplex mode; the other is typically used in a continuous-wave radar, via using the transmitter signal as the local template signal to the oscillator at the receiver, and detecting only the “beat” signal, the difference between the transmitted and received signals. Such designs enable low-complexity and efficient radar sensing. However, they constrain the options for integrating communications functionalities and limit the achievable communication rates. For example, there is large uncertainty with the availability and bandwidth of the beat signal; hence, information conveying will be unreliable. On the other hand, full-duplex operation is still immature for communications, and there is typically clock asynchronism between spatially separated transmitting and receiving nodes. These impose significant limits on integrating radar sensing into communications.
The differences and benefits of JCAS in comparison with individual radar or communication system are summarized in Table 1.2.
Table 1.2 Comparison among radar, communications, and JCAS.
Specifications
Radar
Communications
JCAS system
Signal waveform
Typically simple, unmodulated single-carrier signals occupying large bandwidth; pulse or continuous-wave frequency modulated; orthogonal if multiple spatial streams and orthogonality can be realized in one or more domains of time, frequency, space, and code; typically low PAPR. Radars with advanced waveforms such as
orthogonal frequency-division multiplexing
(
OFDM
) and frequency hopping is also emerging
Mix of unmodulated (pilots/training sequences) and modulated data symbols; Complicated signal structure and resource usage; advanced modulations, e.g.
orthogonal frequency-division multiple access
(
OFDMA
) and multiuser-MIMO; High PAPR
JCAS can use both traditional radar and communication signals, with appropriate modifications to support both communications and sensing and optimize their performance jointly
Tx power
Typically high in large-scale and long-range radar; low in short-range radar such as FMCW radar used in vehicular networks
Typically low, supporting linkage distance up to a few kilometers
Communications integrated into the radar can achieve very long link distance. Sensing integrated into a single communication device can only support short range, but overall JCAS can cover very large areas due to the wide coverage of communication networks
Bandwidth
Large signal bandwidth. Range resolution proportional to bandwidth. But the bandwidth of the output signal in FMCW radar may be narrow, depending on the signal propagation distance
Typically much smaller than radar
mmWave signals are very promising for JCAS, due to large signal bandwidth. In addition, sensing applications do not have to rely on large bandwidth, such as known Wi-Fi sensing applications
Signal band
X, S, C, and Ku
Sub-6 GHz and mmWave bands
Have an impact on operation distances and resolution capabilities of JCAS
Duplex
Full-duplex (continuous-wave radar) or half-duplex (pulse radar)
Colocated transmitter and receiver typically cannot operate on the same time and frequency block. Communications are in either
time division duplex
(
TDD
) or
frequency division duplex
(
FDD
)
Full-duplex is a favorite condition, but not essential
Clock synchronization
Transmitter and receiver are clock-locked
Colocated transmitter and receiver share the same clock, but noncolocated nodes typically do not
Clock-level synchronization removes ambiguity in sensing parameter estimation but is not essential for some sensing applications
1.2.2 Communications-Centric Design
In communications-centric (CC) JCR systems, radio sensing is integrated into existing communication systems as a secondary function. Revision and enhancement to communication infrastructure and systems may be required, but the primary communication signals and protocols largely remain unchanged.
Two fundamental problems in integrating sensing into communications are the following: (i) how to realize full-duplex operation in a monostatic setup where the sensing receiver and transmitter are colocated, and (ii) how to remove the clock asynchronization impact in a bistatic or multistatic setup due to typically unsynchronized clocks between spatially separated transmitters and (sensing) receivers. Full-duplex here means that the receiver and transmitter work at the same time over the same frequency band. For a monostatic radar, full-duplex operation is avoided in pulsed radar via temporally separating the transmitting and receiving timeslots, leading to blind spots in near-field sensing; for FMCW radar, it is realized via using the transmitted signal as the input to the local oscillator to suppress the leakage signal from the transmitter, which leads to the output of the beat-frequency signal with little information on the transmitted signal. Modern communication systems primarily transmit continuous waveform and have unmodulated sinusoidal signals as the input to the oscillator. Hence, both radar methods are not practical in communication systems, unless a dedicated sensing receiver hardware similar to FMCW radar is integrated. In the long term, full-duplex technologies, as have been widely investigated for communications, would be a desired solution for monostatic sensing. However, the technology is still largely immature for practical applications. For bistatic and multistatic radars, clock synchronization is typically realized via wired connections or locking to the GPS signals [26]. These methods are feasible for some communication setups, but lack the generality. In the presence of clock asynchronization, it is also possible to apply signal-processing techniques to overcome it, which will be elaborated in Chapter 4.
Considering the topology of communication networks, CC JCAS systems can be classified into two subcategories, namely, those realizing sensing in point-to-point communication systems particularly for applications in vehicular networks, and those realizing sensing in networks such as mobile/cellular networks. Depending on how the transmitter and sensing receiver are spatially distributed, in terms of sensing, these systems are analog to traditional monostatic, bistatic, and multistatic radars.
There have been numerous reports on sensing in vehicular networks using IEEE 802.11 signals. In [27], the authors implemented active radar sensing functions into a communication system with OFDM signals for vehicular applications. The presented radar sensing functions involve Fourier transform algorithms that estimate the velocity of multiple reflecting objects in IEEE 802.11.p based JCAS system. In [28], automotive radar sensing functions are performed using the single carrier (SC) physical (PHY) frame of IEEE 802.11ad in an IEEE 802.11ad millimeter wave (mmWave) vehicle to vehicle (V2V) communication system. In [29], OFDM communication signals, conforming to IEEE 802.11a/g/p, are used to perform radar functions in vehicular networks. More specifically, a brute-force optimization algorithm is developed based on received mean-normalized channel energy for radar ranging estimation. The processing of delay and Doppler information with IEEE 802.11p OFDM waveform in vehicular networks is shown in [30] by applying the estimation of signal parameters via rotational invariant techniques (ESPRIT) method.
There has been rapidly increasing JCAS work reported for modern mobile networks. In [31], some early work on using OFDM signals for sensing was reported. In [32], sparse array optimization is studied for MIMO JCAS systems. Sparse transmit array design and transmit beampattern synthesis for JCAS are investigated in [33], where antennas are assigned to different functions. In [34], mutual information for an OFDM JCAS system is studied, and power allocation for subcarriers is investigated based on maximizing the weighted sum of the mutual information for communications and sensing. In [35], waveform optimization is studied for minimizing the difference between the generated signal and the desired sensing waveform. In [36], the multiple access performance bound is derived for a multiple antenna JCAS system. In [37], a multicarrier waveform is proposed for dual-use radar-communications, for which interleaved subcarriers or subsets of subcarriers are assigned to the radar or the communications tasks. These studies involve some key signal formats in modern mobile networks, such as MIMO, multiuser MIMO, and OFDM. In [11, 38–41], the authors systematically studied how JCAS can be realized in mobile networks by considering their specific signal, system and network structures, and how radar sensing can be done based on modern mobile communication signals. Such a mobile network with integrated communications and sensing capability is called “perceptive mobile network(PMN).”
In addition to JCAS for mobile networks, sensing using Wi-Fi signals for primarily indoor applications has also received significant research interests in the past 10 years, with various sensing applications being demonstrated. Although mobile and Wi-Fi networks adopt similar modulation technologies, there exist significant differences between them in terms of communication protocols and network topologies, which also lead to different JCAS features. In Table 1.3, we compare the JCAS-versions of the two systems.
Table 1.3 Differences of JCAS–Wi-Fi with respect to JCAS-Mobile (PMN).
Aspects
Wi-Fi networks (with respect to Mobile networks)
Different implications on sensing in Wi-Fi–JCAS (with respect to PMN)
Signal format and transmission
Simpler and flexible packet structure, while PMN has rigid timing and channel structure
Waveform optimization has more flexibility. Available sensing signals are more random in time
Multiuser access
Relatively simpler, while PMN has complicated resource allocation and mixed multiuser access methods
Sensing parameter estimation can be simpler with more optional algorithms
Deployment environment
Mostly indoor and low-speed movement
Richer multipath, but more stable clutter. Less challenging in sensing due to a simpler environment
Network infrastructure and scale
Smaller network. Less-powerful infrastructure such as smaller antenna array
Low potential for networked sensing. Lower-sensing resolution
1.2.3 Radar-Centric Design
Radar systems, particularly military radars, have the extraordinary capability of long-range operation, up to hundreds of kilometers. Therefore, a major advantage of implementing communications in radar systems is the possibility of achieving long-range communications, with much lower latency compared to satellite communications. However, the achievable data rates for such systems are typically limited, due to the inherent limitation in the radar waveform. In [42], the authors implemented a combined radar and communication system based on a software-defined radar platform, in which the radar pulses are used for communications. Research work in [43] and [44] shows that communication network can be potentially established for both static and moving radars used in the military and aviation domains. Adaptive transmit signals from airborne radar mounted on unmanned vehicles can also be used to simultaneously sense a scene and communicate sensed data to a receiver at the ground base station. The objective of such systems is to establish low latency, secure, and long-range communications on top of existing radar systems. Such JCAS systems have been mainly called as dual-function radar-communications (DFRC). For convenience, we will also use it interchangeably with radar-centric (RC) JCAS in this book.
Realization of communications in radar systems has traditionally been based on either pulsed or continuous-wave radar signals. Hence, information embedding is one of the major challenges. Various information embedding techniques have been investigated, and some reviews are available from [2, 4, 16]. The basic principle is that the embedded information should cause little impact on conventional radar operation, while high-speed information transmission is regarded as a design goal. Some of the information embedding techniques are summarized in Table 1.4.
Integrating communications into radar systems with new radar waveforms has also been investigated, such as the MIMO-OFDM radar [49] and frequency-agile (frequency hopping [FH]) radar [47, 48]. Their signal formats are closer to modern communication systems, and hence can be potentially better integrated for information transmission. Such systems typically apply index modulation to embed communication information into the radar waveform. Here, index modulation embeds information to various combinations and/or permutations of signal parameters over space, time, frequency, and code domains [4, 17, 50]. One example is to use the indexes of subcarriers and transmitting antennas to carry the information. The main advantage of applying it in RC JCAS is that index modulation does not change the basic radar waveform and signal structure and has negligible influence on radar operation. We will provide more discussions on this technology in Chapters 7–9.
Table 1.4 Summary of information embedding methods in radar-centric DFRC systems.
Modulations
Methods
Advantages
Disadvantages
Modified waveforms
Time-frequency embedding
Apply various combinations of amplitude, phase and/or frequency shift keying to radar chirp signals, or map data to multiple chirp subcarriers via the use of fractional Fourier Transform
[45]
The chirp signal form remains when the interpulse modulation is used, which is preferred in many radar applications
The waveform can be implemented in many existing radar systems with only modifications to the software
The slow time coding is restricted by the
pulse repetition frequency
(
PRF
) of the radar, thereby limiting the maximum rate of communications
Code-domain embedding
Modulate binary/poly-phased codes in radar signals using direct spread spectrum sequences
[46]
Naturally coexist with the
code-division multiple access/direct sequence spread spectrum
(
CDMA/DSSS
) communication signal form
Enables covert communications by spreading the signal over the bandwidth of radar
Phase modulation will inevitably lead to spectrum alteration of the radar waveform, which may result in energy leakage outside the assigned bandwidth
Spatial embedding
Modulate information bits to the sidelobes of the radar beampattern
Has little impact on the radar-sensing performance in the mainlobe
The performance is sensitive to the accuracy of array calibration and BF
The multipath of radar signal may incur interference to the communications
Index modulation (no waveform modification)
Represent information by the indexes of antennas, frequencies, and/or codes of the signals [
47
,
48
]
Naturally coexist with the radar functionality, with negligible impact on radar performance
Generally achieve higher-data rates compared to modulation with modified waveform
Demodulation may be complicated
Demodulation performance is sensitive to channel if IM is applied to spatial domain
Codebook design could be a challenge
1.2.4 Joint Design without an Underlying System
Although there is no clear boundary between the third category of technologies and systems and the previous two categories, there is more freedom for the former in terms of signal and system design. That is, JCAS technologies can be developed without being limited to existing communication or radar systems. In this sense, they can be designed and optimized by considering the essential requirements for both communications and sensing, potentially providing a better trade-off between the two functions.
The mmWave and (sub-)Terahertz-wave JCAS systems are great examples of facilitating such joint design. On the one hand, with their large bandwidth and short wavelength, mmWave and Terahertz signals provide great potentials for high date-rate communications and high-accuracy sensing. On the other hand, mmWave and Terahertz systems are emerging. They are yet to be widely deployed, and the standards for Terahertz systems are yet to be developed. Millimeter and Terahertz JCAS can facilitate many new exciting applications, both indoor and outdoor. Existing research on mmWave JCAS has demonstrated its feasibility and potentials in indoor and vehicle networks [13, 23, 51]. The authors in [13] provide an in-depth discussion on the signal processing aspect of mmWave-based JCAS with an emphasis on waveform design for JCAS systems. Future mmWave JCAS for indoor sensing is envisioned in [52]. Hybrid beamforming design for mmWave JCAS systems is investigated in [53]. An adaptive mmWave waveform structure is designed in [54]. Design and selection of JCAS waveforms for automotive applications are investigated in [51], where comparisons between phase-modulated continuous-wave JCAS and OFDMA-based JCAS waveforms are provided by analyzing the system model and enumerating the impact of design parameters. In [23, 55], multibeam technologies are developed to allow communications and sensing at different directions, using a common transmitted signal. Beamforming vectors are designed and optimized to enable fast beam update and achieve balanced performance between communications and sensing. In [56], the beamforming design for Terahertz massive MIMO JCAS systems is investigated.
Another example is multichannel JCAS systems where one or more channels are used at a time, and multiple channels are occupied over a period of signal transmission. One specific example is the frequency hopping system, such as the existing Bluetooth system where the operating frequency channel is changed over different packets. Multichannel systems can offer an overall large signal bandwidth for sensing without increasing the instantaneous communication bandwidth. This can largely reduce the hardware cost and also match with the spectrum usage of communication systems well. Works on combining multichannel signals for sensing have been reported for passive radar in, e g. [57, 58]. The key challenge is how to remove or reduce the imperfections and distortions from the received signals for each channel and then concatenate them together for sensing. For JCAS, an additional important problem is how to design the signals to make such concatenation easier, while balancing the performance of communications and sensing.
1.2.5 Summary of Key Research Problems
We conclude this section by briefly summarizing some key research problems and the associated challenges for the three types of JCAS systems. The summary is presented in Tables 1.5 and 1.6.
1.3 Potential Sensing Applications of JCAS
With harmonized and integrated communications and sensing functions, JCAS systems are expected to have the following advantages:
Spectral efficiency
