Integrated Sensing and Communications for Future Wireless Networks

Integrated Sensing and Communications for Future Wireless Networks: Principles, Advances and Key Enabling Technologies presents the principles, methods, and algorithms of ISAC, an overview of the essential enabling technologies, as well as the latest research and future directions. Suitable for academic researchers and post graduate students as well as industry R&D engineers, this book is the definitive reference in this interdisciplinary field that is being seen as a technology to enable emerging applications such as vehicular networks, environmental monitoring, remote sensing, IoT, smart cities.

Importantly, ISAC has been identified as an enabling technology for B5G/6G, and the next-generation Wi-Fi system. ISAC brings together a range of technologies: radar sensing, reconfigurable intelligent surfaces, holographic surfaces through to high frequency terahertz, PHY security, channel signaling, multiple access, and machine learning.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Contributors
About the editor
Acknowledgments
Chapter 1: Introduction to integrated sensing and communications for the next generation of wireless connectivity
1.1. Introduction
1.2. A historical overview on ISAC
1.2.1. Initial development of radars
1.2.2. Mutual inspiration between radar and communication
1.2.3. Radar and communication development in parallel
1.2.4. Convergence of sensing and communications integration
1.3. ISAC: a paradigm shift in wireless system design
1.3.1. Waveform design for ISAC
1.4. ISAC applications in industry and academia
1.4.1. Case studies of applications
1.4.2. ISAC progress in industry and standardization
1.5. Integration of ISAC and other next-generation wireless technologies
1.5.1. ISAC and edge intelligence
1.5.2. ISAC and reconfigurable intelligent surfaces (RIS)
1.5.3. ISAC and aerial non-terrestrial networks (NTN)
1.5.4. ISAC and low-Earth-orbit (LEO) satellite network
1.5.5. ISAC with cmWave and sub-THz frequencies
1.5.6. ISAC using artificial intelligence and machine learning
1.6. Conclusions
Part 1: Intelligent metasurfaces for ISAC
Chapter 2: Holographic integrated sensing and communications
2.1. Introduction
2.2. RHS essentials
2.2.1. Hardware
2.2.2. Principles
2.3. Holographic beamforming scheme for ISAC
2.3.1. Scenario description
2.3.2. Holographic beamforming structure
2.4. Holographic integrated sensing and communication problem formulation
2.4.1. Performance metrics
2.4.2. Problem formulation
2.4.3. Problem decomposition
2.5. Holographic beamforming optimization algorithm design
2.5.1. Optimization of digital beamforming
2.5.2. Optimization of analog beamforming
2.5.3. Overall algorithm
2.6. Performance analysis
2.6.1. Beampattern gain analysis
2.6.2. Convergence and complexity
2.6.3. Cost-effectiveness analysis
2.6.4. Energy-performance trade-off
2.7. Simulation results
2.7.1. Holographic beamforming performance
2.7.2. Comparison with the HAD-based MIMO scheme
2.7.3. Impact of the RHS on the system performance
2.8. Experimental results
2.8.1. Prototype of the holographic ISAC system
2.8.2. Hardware modules of the holographic ISAC prototype
2.8.3. Performance evaluation
2.9. Discussion
2.9.1. Fundamental designs of the RHS
2.9.2. Limitations and trade-offs of holographic ISAC
2.9.3. Optimization of holographic ISAC transceiver
2.10. Conclusion
Appendix 2.A. Proof of Proposition 2.1
Appendix 2.B. Proof of Proposition 2.2
Appendix 2.C. Proof of Proposition 2.3
Appendix 2.D. Proof of Proposition 2.4
Chapter 3: Reconfigurable intelligent surface empowered integrated sensing and communications
3.1. Introduction of RIS
3.1.1. Hardware architecture
3.1.2. Signal propagation model
3.1.3. Potential and advantage
3.2. RIS-assisted communication/sensing
3.2.1. RIS-assisted communication systems
3.2.2. RIS-assisted radar sensing systems
3.3. RIS-assisted ISAC systems
3.3.1. RIS in RCC/DFRC systems
3.3.2. Case study
3.3.3. Various RIS deployments in ISAC systems
3.3.4. Near-field RIS-assisted ISAC systems
3.4. Future research directions
3.4.1. Theoretical analysis and practical measurement
3.4.2. Extended RIS-assisted ISAC scenarios
3.4.3. Algorithm designs
3.5. Conclusion
Part 2: Machine learning and AI for ISAC
Chapter 4: Channel estimation in ISAC-IRS systems using machine learning approaches
4.1. Introduction
4.1.1. ISAC concept
4.1.2. IRS concept
4.1.3. Challenges in ISAC-IRS systems
4.1.4. Channel estimation in IRS-assisted communication systems
4.1.5. Channel estimation in IRS-assisted ISAC systems
4.2. Supervised-machine learning (S-ML) channel estimation in IRS-assisted communication systems
4.2.1. S-ML concept
4.2.2. Applications of S-ML to IRS channel estimation
4.3. S-ML channel estimation in IRS-assisted ISAC systems
4.3.1. Application 1: single-user
4.3.2. Application 2: multi-user case 1
4.3.3. Application 3: multi-user case 2
4.4. Future directions and research challenges
4.5. Conclusion
Chapter 5: Integrated ISAC and federated learning in wireless edge networks
5.1. ISAC-aided FL design
5.1.1. Basic principles
5.1.2. Examples
5.2. FL-enhanced ISAC capacity
5.2.1. Basic principles
5.2.2. Examples
5.3. Challenges and prospects
5.3.1. Challenges
5.3.2. Prospects
Part 3: ISAC waveform design and full-duplex
Chapter 6: Communication-centric multi-metric ISAC waveform optimization
6.1. Introduction
6.2. Radar metrics
6.3. Communication metrics
6.4. Joint radar-communication waveform optimization
6.4.1. Weighted sum optimization method
6.4.2. Euclidean distance optimization method
6.4.3. Threshold constrained optimization method
6.4.4. Decoupled optimization method
6.5. Case study
6.5.1. System model
6.5.2. ISAC waveform optimization problems
6.5.3. Simulation results
6.6. Conclusions
Chapter 7: Waveform design with constant peak-to-average power ratio
7.1. Introduction
7.1.1. Background
7.1.2. Peak-to-average power ratio
7.1.3. Radar waveforms
7.2. Novel ISAC waveform design
7.2.1. Radar sensing operation
7.2.2. Communications operation
7.3. Permutation based waveform design
7.3.1. Radar sensing operation for permutation based waveform
7.3.2. Communications operation for permutation based waveform
7.4. Subset selection
7.4.1. Communication functionality based subsets
7.4.2. Radar sensing functionality based subsets
7.5. Conclusion and future research directions
Chapter 8: Detection of integrated sensing and communications waveforms
8.1. Introduction
8.2. ISAC system and JRC application
8.3. Design and detection of ISAC waveforms
8.3.1. Chirp-based ISAC waveforms
8.3.2. OFDM-based ISAC waveforms
8.3.3. OTFS-based ISAC waveforms
8.4. Non-coherent discrete chirp Fourier transform (NC-DCFT) algorithm for ISAC waveform detection
8.4.1. ISAC waveform – data modulated LFM signal
8.4.2. DCFT method and modified DCFTs
8.4.3. Non-coherent DCFT algorithm
8.4.4. Random sampling for NC-DCFT
8.5. Information-theoretic compressive sensing for ISAC waveforms
8.5.1. Background
8.5.2. Applications in radar sensing
8.5.3. Towards information-theoretic compressive sensing for ISAC waveforms
8.6. Conclusion
CRediT authorship contribution statement
Chapter 9: Full duplex massive MIMO for integrated sensing and communications
9.1. Introduction
9.2. Related literature
9.3. Full duplex massive MIMO system model
9.3.1. Transceiver architectures
9.3.2. Received signal models
9.4. Channel models
9.4.1. Downlink MIMO channels
9.4.2. End-to-end MIMO channel from signal reflections
9.5. Full duplex massive MIMO ISAC
9.5.1. Estimation of targets' parameters
9.5.2. FD-enabled ISAC optimization formulation
9.6. Numerical results and discussions
9.7. Conclusion
Chapter 10: Linear transmit waveform and radar receive beamforming design for joint radar-communication systems
10.1. Introduction
10.1.1. Motivations and prior works
10.1.2. Our contributions
10.2. System model description
10.2.1. Communication model
10.2.2. Radar model
10.3. Problem formulation
10.3.1. Total transmit power minimization subject to communication and radar QoS constraints
10.3.2. Maximization of the minimum communication SINR subject to radar QoS and total transmit power constraints
10.3.3. Maximization of radar output SINR subject to communication and total transmit power constraints
10.4. Solutions
10.4.1. Total transmit power minimization subject to communication and radar QoS constraints
10.4.2. Maximization of the minimum communication SINR subject to radar QoS and total transmit power constraints
10.4.3. Maximization of radar output SINR subject to communication and total transmit power constraints
10.5. Numerical results
10.6. Conclusion
Part 4: Millimeter wave, terahertz and beamforming for ISAC
Chapter 11: Beamforming architectures for integrated sensing and communications in millimeter-wave and terahertz
11.1. Introduction
11.2. System model and problem formulation
11.2.1. Communications model
11.2.2. Radar model
11.2.3. Problem formulation
11.3. Hybrid beamformer design for ISAC
11.4. Beam split correction
11.5. Numerical experiments
11.6. Conclusions and future outlook
Chapter 12: Flexible hybrid precoding for integrated sensing and communications
12.1. Introduction
12.2. MIMO channel models
12.2.1. Communications channel
12.2.2. Radar channel
12.2.3. Joint communications and radar channel
12.3. Mutual information for ISAC
12.3.1. Capacity of independent channels
12.3.2. Capacity of joint communications and radar channels
12.4. Hybrid precoder design
12.4.1. Precoding for joint radar and communications
12.5. Flexible precoder design
12.5.1. Communications-only flexible beamformer
12.5.2. Joint radar and communications flexible beamformer
12.5.3. Blind flexible beamforming
12.6. Evaluation results
Part 5: Network architectural aspects of integrating sensing into cellular systems
Chapter 13: RAN architecture and implementation requirements for ISAC
13.1. Introduction
13.2. Overview of 3GPP and O-RAN architectures
13.3. Sensing and communication signals and air interface aspects
13.4. Sensing and communication radio design and signal processing aspects
13.4.1. Capabilities and requirements of radio unit
13.4.2. Signal processing and capabilities of distributed unit
13.4.3. Receiver algorithm for sensing bandwidth aggregation
13.5. Requirements of fronthaul throughput and RU–DU split
13.6. Challenges
13.7. Conclusion
Part 6: Multiple access and interference management for ISAC
Chapter 14: Multiple access for integrated sensing and communication
14.1. Introduction
14.2. ISAC: evolution, models, and comparison
14.2.1. From orthogonal ISAC to non-orthogonal ISAC
14.2.2. Different MA-assisted O-ISAC systems
14.2.3. Different MA-assisted NO-ISAC systems
14.3. Metrics and system performance
14.3.1. Metrics for communication
14.3.2. Metrics for radar
14.3.3. System performance comparison
14.4. Applications of MA-assisted ISAC
14.4.1. ISAC-aided V2X
14.4.2. ISAC with remote sensing
14.4.3. ISAC with mmWave and THz
14.4.4. ISAC meets edge intelligence
14.4.5. ISAC supported by RIS
14.4.6. ISAC with SAGIN
14.4.7. ISAC in near field
14.4.8. ISAC enabled industrial IoT
Chapter 15: Self-interference suppression techniques in integrated sensing and communication systems: an overview
15.1. Introduction
15.1.1. Motivation and objectives
15.1.2. Chapter organization
15.2. Mechanism of self-interference and effects
15.3. Classification of the SIS techniques
15.3.1. Analog SIS
15.3.2. Digital SIS
15.3.3. Analog–digital hybrid SIS
15.3.4. Passive SIS
15.4. ISAC
15.5. Techniques for SIS in ISAC
15.6. Future outlook
15.7. Conclusion
Part 7: Conclusion and future prospects of ISAC technology
Chapter 16: ISAC standardization and synergies with key technology enablers: overview and future prospects
16.1. ISAC: overview and potential benefits
16.1.1. Overview: the holistic perspective
16.1.2. Transformative benefits
16.1.3. Support for modern applications
16.2. Synergies with key technology enablers and promising applications
16.3. Current research focus and standardization efforts
16.3.1. 3GPP
16.3.2. Other global organizations/forums
16.4. Challenges and future perspectives
16.4.1. Compliance and regulatory considerations
16.4.2. Cost effectiveness and power management
16.4.3. Envisioned use cases and applications
16.5. Conclusion
Index

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Integrated Sensing and Communications for Future Wireless Networks

Principles, Advances and Key Enabling Technologies

Purchase options

Shaping today's innovations

Institutional subscription on ScienceDirect

Aryan Kaushik