Reconfigurable Intelligent Surface-Enabled Integrated Sensing and Communication in 6G (Wireless Networks)

✍ Scribed by Xiaoling Hu, Chenxi Liu, Mugen Peng, Caijun Zhong

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 186
• Category: Library

✦ Synopsis

As the main trend and key enabling technology for next-generation wireless networks (i.e., 6G), integrated sensing and communication (ISAC) can effectively improve spectrum efficiency, hardware efficiency, and information processing efficiency. However, it faces several deficiencies, including limited coverage due to high-frequency signals and limited communication-sensing performance due to uncontrollable wireless environments. Reconfigurable intelligent surface (RIS) provides novel dimensions to address these deficiencies by intelligently manipulating the wireless propagation environment in an energy- and hardware-efficient manner. RIS-enabled ISAC is expected to comprehensively promote the multi-dimensional performance of 6G, such as communication capacity, sensing accuracy, and coverage. Nevertheless, to fully realize its potential, one needs to figure out the impacts of RIS on joint communication and sensing performance and tackle new technical challenges in beamforming designand signal processing. The goal of this book, therefore, is to deliver a thorough understanding of RIS-enabled ISAC from three perspectives: performance analysis, beamforming design, and signal processing. Specifically, the authors provide a brief introduction to RIS-enabled ISAC, including basic concepts, motivations, potential application scenarios, and an overview of the state-of-the-art research on RIS-enabled ISAC. The theoretical performance analytical frameworks of RIS-enabled ISAC and their corresponding results are also discussed. Based on this, several critical issues are identified and elaborated on, including signal processing technologies such as angle and Delay-Doppler information acquisition, and air interface technologies such as beamforming designs. Finally, the book concludes with future trends and open issues for further research.

The book would be beneficial for researchers, graduate students, and industry professionals who wish to gain a comprehensive understanding of the latest developments and challenges in RIS-enabled ISAC. By providing insights into the potential of RIS-enabled ISAC and the technical challenges that need to be addressed, the book can aid in the development of practical solutions for next-generation wireless networks and contribute to the advancement of the field of wireless communications.

✦ Table of Contents

Preface
Contents
Acronyms
1 Introduction of RIS-Enabled ISAC
1.1 Background and Motivations of RIS-Enabled ISAC
1.1.1 Requirements of 6G
1.1.2 Basics and Challenges of ISAC
1.1.3 Basics and Advantages of RIS
1.1.4 Motivations of RIS-Enabled ISAC
1.2 Potential Applications of RIS-Enabled ISAC
1.2.1 Smart Transportation
1.2.2 Smart Factory
1.2.3 UAV Application
1.3 Fundamentals of RIS-Enabled ISAC
1.3.1 RIS Response Model
1.3.2 Signal Model
1.3.2.1 Communication Signal Model
1.3.2.2 Sensing Signal Model
1.3.3 Channel Model
1.3.3.1 Far-Field Model
1.3.3.2 Near-Field Model
1.4 Overview of RIS-Enabled ISAC
1.4.1 RIS-Enabled DF ISAC
1.4.1.1 RIS-Enabled RCC
1.4.1.2 RIS-Enabled DFRC
1.4.2 RIS-Enabled DB ISAC
1.4.3 Summary
References
2 Theoretical Performance Analysis of RIS-Enabled ISAC
2.1 Introduction
2.2 System Model of RIS-Enabled NO-ISAC
2.2.1 NO-ISAC Transmission Protocol
2.2.2 Channel Model
2.2.3 Signal Model
2.3 Performance Evaluation of RIS-Enabled NO-ISAC System
2.4 Modified CRLB-Based Beamforming Design
2.4.1 BS Active Beamforming
2.4.2 IRS Passive Beamforming
2.5 Numerical Results and Discussions on the Performance of RIS-Enabled ISAC
2.5.1 RIS-Enabled NO-ISAC System vs. RIS-Enabled Localization System
2.5.2 RIS-Enabled NO-ISAC System vs. RIS-Enabled TD-ISAC System
2.5.3 Tradeoff Between Communication Performance and Localization Performance
2.6 Summary
References
3 Angle Information Acquisition in RIS-Enabled ISAC
3.1 Introduction
3.2 Single-User Localization via AoA Estimation
3.2.1 System Model
3.2.1.1 Signal Model
3.2.1.2 Channel Model
3.2.2 Proposed Location Sensing Scheme
3.2.2.1 Estimation of Effective AoAs
3.2.2.2 Estimation of User's Location
3.2.3 Numerical Results and Discussions
3.3 Simultaneous Multi-User Localization via AoA Estimation
3.3.1 System Model
3.3.1.1 Channel Model
3.3.1.2 Signal Model
3.3.2 Multi-User Location Sensing
3.3.2.1 Estimate Effective AoAs
3.3.2.2 Determine Users' Locations
3.3.3 Numerical Results and Discussions
3.4 Summary
References
4 Delay-Doppler Information Acquisition in RIS-Enabled ISAC
4.1 Introduction
4.2 System Model
4.2.1 Transmission Protocol
4.2.2 Channel Model
4.2.3 Signal Model
4.3 Coarse-Grained Sensing via Delay-Doppler Estimation
4.3.1 Hierarchical Codebook Design
4.3.2 3D Hierarchical Beam Training Based on a DSP Detector
4.3.2.1 Test Statistic Selection
4.3.2.2 Target Detection
4.3.3 Beam Refinement for High-Accuracy Direction Estimation
4.4 Fine-Grained Sensing via Delay-Doppler Estimation
4.4.1 Maximum Likelihood Estimation
4.5 Numerical Results and Discussions
4.5.1 Performance of the Hierarchical RIS 3D Beam Training
4.5.2 Performance of the Distance and Velocity Estimation
4.6 Summary
References
5 Sensing-Assisted Beamforming in RIS-Enabled ISAC
5.1 Introduction
5.2 Sensing-Assisted Beamforming in the Single-User Case
5.2.1 System Model
5.2.1.1 ISAC Transmission Protocol
5.2.1.2 Signal Model
5.2.1.3 Channel Model
5.2.2 Beamforming Design
5.2.2.1 ISAC Period
5.2.2.2 PC Period
5.2.3 Numerical Results and Discussions
5.2.3.1 Performance of the Proposed Beamforming Algorithms
5.2.3.2 Performance of the Proposed ISAC Transmission Protocol
5.3 Sensing-Assisted Beamforming in the Multi-User Case
5.3.1 System Model
5.3.1.1 Transmission Protocol of the RIS-Enabled ISAC System
5.3.1.2 Channel Model
5.3.1.3 Signal Model
5.3.2 Sensing-Based Joint Active and Passive Beamforming
5.3.2.1 ISAC Period
5.3.2.2 PC Period
5.3.3 Numerical Results and Discussions
5.3.3.1 Performance of the Proposed Sensing-Based Beamforming Algorithms
5.3.3.2 Performance of the RIS-Enabled Multi-User ISAC System
5.4 Summary
References
6 Beamforming for Simultaneous Communication and Sensing Enhancement
6.1 Introduction
6.2 Beamforming for Joint Performance Enhancement of Device-Based RIS-ISAC
6.2.1 System Model of Device-Based RIS-ISAC
6.2.1.1 ISAC Transmission Protocol
6.2.1.2 Channel Model
6.2.2 Sensing-Based Beamforming for Joint Performance Enhancement
6.2.2.1 ECSI-Based Beamforming in Phase 1
6.2.2.2 Sensing-Based Beamforming in Phase 2
6.2.3 Numerical Results and Discussions
6.2.3.1 Proposed RIS-Enabled ISAC System vs. RIS-Enabled Communication System
6.2.3.2 How Does Beamforming Affect the Communication-Sensing Trade-Off?
6.2.3.3 How Does the Sensing Accuracy Affect ISAC Beamforming?
6.3 Beamforming for Joint Performance Enhancement of Device-Free RIS-ISAC
6.3.1 System Model
6.3.1.1 Channel Model
6.3.1.2 Signal Model
6.3.2 Wideband Beamforming for Simultaneous Communication and Sensing Enhancement
6.3.2.1 Optimization of the Sensing Subarray
6.3.2.2 Optimization of the Communication Subarray
6.3.2.3 Alternating Optimization Algorithm
6.3.3 Numerical Results
6.4 Summary
References
7 Future Trends and Open Issues
7.1 New Information Theory and Practical Measurement
7.1.1 New Information Theory
7.1.2 Practical Measurement
7.2 Air Interface Technologies
7.2.1 Waveform Design
7.2.2 Beamforming
7.3 Signal Processing Technologies
7.4 Hardware Design and Practical Deployments
7.4.1 Wideband and High-Frequency the RIS
7.4.2 Deployment and Control of the RIS
References