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

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