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Table of Contents
Intro
Contents
Background and Fundamentals for ISAC
Integrated Sensing and Communications: Background and Applications
1 Introduction
1.1 Integration and Coordination Gains
1.2 Design Philosophy
2 The Interplay Between S&C
2.1 ISAC: From Resource Competition to Co-design
2.2 The Driving Forces
3 Use Cases
3.1 Sensing as a Service
3.2 Smart Home and In-Cabin Sensing
3.3 Vehicle to Everything (V2X)
3.4 Smart Manufacturing and Industrial IoT
3.5 Remote Sensing and Geoscience
3.6 Environmental Monitoring
3.7 Human Computer Interaction (HCI)
4 Industry Progress and Standardization
5 Conclusions
References
Fundamental Limits for ISAC: Information and Communication Theoretic Perspective
1 Introduction
2 Performance Metrics
2.1 Communication and Estimation Rates
2.2 Communication and Estimation MSE
2.3 Capacity-Distortion Tradeoff
2.4 Rate-Error-Exponent Region
3 Information-Theoretic Limits of Point-to-Point ISAC Channels
3.1 Capacity-Distortion Tradeoff
3.2 Extensions
4 Information-Theoretic Limits of Multi-user ISAC Channels
4.1 Broadcast ISAC Channels
4.2 Multiple-access ISAC Channels
5 Open Problems and Future Research Directions
5.1 Fundamental Limits of ISAC Under More General Setup
5.2 Joint Communication and Recognition/Classification
5.3 Environment Side Information Aided ISAC
5.4 Artificial Intelligence (AI)-Aided ISAC
References
Fundamental Limits for ISAC-Radar Perspective
1 Introduction
2 Signal Model
3 Distributed Radar Parameter Estimation
3.1 Received Signal Quantization-based Method
3.2 Time Delay Quantization-based Method
4 Approximation of Quantization Output
5 CRB for Parameter Estimation Under Quantization Approximation
5.1 Received Signal Quantization-based Method
5.2 Time Delay Quantization-based Method
6 Performance Analysis
7 Simulation
7.1 Parameter Estimation
7.2 Comparison of the Two Methods
8 Conclusions
References
Fundamental Limits for ISAC-Localization Perspective
1 Introduction
2 System Model
2.1 Device-Based Localization
2.2 Device-Free Localization
2.3 Fisher Information Analysis
3 Performance Analysis of Device-Based Networks
3.1 Static Scenario
3.2 Dynamic Scenario
3.3 Analysis of Non-ideal Factors
4 Performance Analysis of Device-Free Localization Networks
4.1 Static Scenario
4.2 Dynamic Scenario
4.3 Analysis of Non-ideal Factors
5 Numercial Results
5.1 Device-Based Localization Network
5.2 Device-Free Localization Network
6 Conclusion
References
Fundamental Limits for ISAC-Asymptotics in Massive MIMO Sensing Systems
1 Introduction
2 Massive MIMO Radar System Description
2.1 Signal Model
2.2 A Robust Wald-Type Test for Target Detection
3 Introduction to Reinforcement Learning
3.1 Markov Decision Process (MDP)
3.2 The SARSA Learning Algorithm
Contents
Background and Fundamentals for ISAC
Integrated Sensing and Communications: Background and Applications
1 Introduction
1.1 Integration and Coordination Gains
1.2 Design Philosophy
2 The Interplay Between S&C
2.1 ISAC: From Resource Competition to Co-design
2.2 The Driving Forces
3 Use Cases
3.1 Sensing as a Service
3.2 Smart Home and In-Cabin Sensing
3.3 Vehicle to Everything (V2X)
3.4 Smart Manufacturing and Industrial IoT
3.5 Remote Sensing and Geoscience
3.6 Environmental Monitoring
3.7 Human Computer Interaction (HCI)
4 Industry Progress and Standardization
5 Conclusions
References
Fundamental Limits for ISAC: Information and Communication Theoretic Perspective
1 Introduction
2 Performance Metrics
2.1 Communication and Estimation Rates
2.2 Communication and Estimation MSE
2.3 Capacity-Distortion Tradeoff
2.4 Rate-Error-Exponent Region
3 Information-Theoretic Limits of Point-to-Point ISAC Channels
3.1 Capacity-Distortion Tradeoff
3.2 Extensions
4 Information-Theoretic Limits of Multi-user ISAC Channels
4.1 Broadcast ISAC Channels
4.2 Multiple-access ISAC Channels
5 Open Problems and Future Research Directions
5.1 Fundamental Limits of ISAC Under More General Setup
5.2 Joint Communication and Recognition/Classification
5.3 Environment Side Information Aided ISAC
5.4 Artificial Intelligence (AI)-Aided ISAC
References
Fundamental Limits for ISAC-Radar Perspective
1 Introduction
2 Signal Model
3 Distributed Radar Parameter Estimation
3.1 Received Signal Quantization-based Method
3.2 Time Delay Quantization-based Method
4 Approximation of Quantization Output
5 CRB for Parameter Estimation Under Quantization Approximation
5.1 Received Signal Quantization-based Method
5.2 Time Delay Quantization-based Method
6 Performance Analysis
7 Simulation
7.1 Parameter Estimation
7.2 Comparison of the Two Methods
8 Conclusions
References
Fundamental Limits for ISAC-Localization Perspective
1 Introduction
2 System Model
2.1 Device-Based Localization
2.2 Device-Free Localization
2.3 Fisher Information Analysis
3 Performance Analysis of Device-Based Networks
3.1 Static Scenario
3.2 Dynamic Scenario
3.3 Analysis of Non-ideal Factors
4 Performance Analysis of Device-Free Localization Networks
4.1 Static Scenario
4.2 Dynamic Scenario
4.3 Analysis of Non-ideal Factors
5 Numercial Results
5.1 Device-Based Localization Network
5.2 Device-Free Localization Network
6 Conclusion
References
Fundamental Limits for ISAC-Asymptotics in Massive MIMO Sensing Systems
1 Introduction
2 Massive MIMO Radar System Description
2.1 Signal Model
2.2 A Robust Wald-Type Test for Target Detection
3 Introduction to Reinforcement Learning
3.1 Markov Decision Process (MDP)
3.2 The SARSA Learning Algorithm