From Fundamentals to Advanced Techniques
Buch, Englisch, 320 Seiten, Format (B × H): 157 mm x 235 mm, Gewicht: 612 g
ISBN: 978-1-119-98291-3
Verlag: Wiley
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.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Acknowledgments xiii
Preface xv
Acronyms xvii
Part I Fundamentals of Joint Communications and
Sensing (JCAS) 1
1 Introduction to Joint Communications and Sensing
(JCAS) 3
1.1 Background 3
1.2 Three Categories of JCAS Systems 5
1.2.1 Major Differences Between Communications and Sensing 7
1.2.2 Communications-Centric Design 12
1.2.3 Radar-Centric Design 15
1.2.4 Joint Design without an Underlying System 17
1.2.5 Summary of Key Research Problems 18
1.3 Potential Sensing Applications of JCAS 18
1.4 Book Organization 22
References 24
2 Signal Processing Fundamentals for JCAS 31
2.1 Channel Model for Communications and Radar 31
2.2 Basic Communication Signals and Systems 33
2.2.1 Single-Carrier MIMO 33
2.2.2 MIMO-OFDM 34
2.2.3 Transmitter and Receiver Signal Processing in Communications 34
2.3 MIMO Radar Signals and Systems 36
2.3.1 Single-Carrier MIMO Radar 36
2.3.2 MIMO-OFDM Radar 37
2.3.3 FH-MIMO Radar 38
2.4 Basic Signal Processing for Radar Sensing 40
2.4.1 Matched Filtering 40
2.4.2 Moving Target Detection (MTD) 41
2.4.3 Spatial-Domain Processing 42
2.4.4 Target Detection 43
2.4.5 Spatial Refinement 44
2.5 Signal Processing Basics for Communication-Centric JCAS 44
2.5.1 802.11ad JCAS Systems 44
2.5.2 Mobile Network with JCAS Capabilities 46
2.5.3 Sensing Parameter Estimation 46
2.5.3.1 Direct and Indirect Sensing 47
2.5.3.2 Sensing Algorithms 49
2.6 Signal Processing Basics for DFRC 50
2.6.1 Embedding Information in RadarWaveform 50
2.6.2 Signal Reception and Processing for Communications 52
2.6.2.1 Demodulation 53
2.6.2.2 Channel Estimation 54
2.6.3 Codebook Design 54
2.7 Conclusions 55
References 55
3 Efficient Parameter Estimation 59
3.1 Q-Shifted Estimator (QSE) 60
3.2 Refined QSE (QSEr) 62
3.2.1 Impact ofq 62
3.2.2 Refined Optimalq 66
3.2.3 Numerical Illustration of QSEr 67
3.3 Padé approximation-Enabled Estimator 70
3.3.1 Core Updating Function 71
3.3.2 Initialization and Overall Estimation Procedure 74
3.3.3 Numerical Illustrations 76
3.4 Conclusions 80
References 80
Part II Communication-Centric JCAS 83
4 Perceptive Mobile Network (PMN) 85
4.1 Framework for PMN 85
4.1.1 System Platform and Infrastructure 86
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Contents vii
4.1.1.1 CRAN 87
4.1.1.2 Standalone BS 87
4.1.2 Three Types of Sensing Operations 88
4.1.2.1 Downlink Active Sensing 88
4.1.2.2 Downlink Passive Sensing 88
4.1.2.3 Uplink Sensing 89
4.1.2.4 Comparison 89
4.1.3 Signals Usable from 5G NR for Radio Sensing 90
4.1.3.1 Reference Signals Used for Channel Estimation 90
4.1.3.2 Nonchannel Estimation Signals 92
4.1.3.3 Data Payload Signals 92
4.2 System Modifications to Enable Sensing 92
4.2.1 Dedicated Transmitter for Uplink Sensing 93
4.2.2 Dedicated Receiver for Downlink (and Uplink) Sensing 94
4.2.3 Full-Duplex Radios for Downlink Sensing 94
4.2.4 Base Stations with Widely Separated Transmitting and Receiving
Antennas 96
4.3 System Issues 98
4.3.1 Performance Bounds 98
4.3.2 Waveform Optimization 100
4.3.2.1 Spatial Optimization 102
4.3.2.2 Optimization in Time and Frequency Domains 105
4.3.2.3 Optimization with Next-Generation Signaling Formats 106
4.3.3 Antenna Array Design 106
4.3.3.1 Virtual MIMO and Antenna Grouping 107
4.3.3.2 Sparse Array Design 108
4.3.3.3 Spatial Modulation 109
4.3.3.4 Reconfigurable Intelligent Surface-Assisted JCAS 109
4.3.4 Clutter Suppression Techniques 110
4.3.4.1 Recursive Moving Averaging (RMA) 112
4.3.4.2 Gaussian Mixture Model (GMM) 113
4.3.5 Sensing Parameter Estimation 114
4.3.5.1 Periodogram such as 2D DFT 115
4.3.5.2 Subspace-Based Spectrum Analysis Techniques 115
4.3.5.3 On-Grid Compressive Sensing Algorithms 117
4.3.6 Resolution of Sensing Ambiguity 119
4.3.7 Pattern Analysis 122
4.3.8 Networked Sensing under Cellular Topology 123
4.3.8.1 Fundamental Theories and Performance Bounds for “Cellular Sensing
Networks” 123
4.3.8.2 Distributed Sensing with Node Grouping and Cooperation 124
4.3.9 Sensing-Assisted Communications 124
4.3.9.1 Sensing-Assisted Beamforming 124
4.3.9.2 Sensing-Assisted Secure Communications 128
4.4 Conclusions 128
References 128
5 Integrating Low-Complexity and Flexible Sensing into
Communication Systems: A Unified Sensing
Framework 139
5.1 Problem Statement and Signal Model 139
5.1.1 Signal Model 140
5.1.2 Classical OFDM Sensing (COS) 142
5.1.3 Problem Statement 143
5.1.3.1 CP-limited Sensing Distance 143
5.1.3.2 Communication-limited Velocity measurement 143
5.1.3.3 COS adapted for DFT-S-OFDM 144
5.2 A Low-Complexity Sensing Framework 144
5.3 Performance Analysis 150
5.3.1 Preliminary Results 150
5.3.2 Analyzing Signal Components in Two RDMs 151
5.3.3 Comparison and Insights 154
5.3.4 Criteria for Setting Key Sensing Parameters 157
5.4 Simulation Results 158
5.4.1 Illustrating SINRs in RDMs 159
5.4.2 Illustration of Target Detection 162
5.5 Conclusions 166
References 167
6 Sensing Framework Optimization 169
6.1 Echo Preprocessing 169
6.1.1 Reshaping 170
6.1.2 Virtual Cyclic Prefix (VCP) 171
6.1.3 Removing Communication Information 174
6.2 Target Parameter Estimation 177
6.2.1 Parameter Estimation Method 177
6.2.2 Computat
