Intelligent Surfaces Empowered 6G Wireless Network

Cover
Title Page
Copyright
Contents
About the Editors
List of Contributors
Preface
Acknowledgement
Part I Fundamentals of IRS
Chapter 1 Introduction to Intelligent Surfaces
	1.1 Background
	1.2 Concept of Intelligent Surfaces
	1.3 Advantages of Intelligence Surface
	1.4 Potential Applications
	1.5 Conclusion
	Bibliography
Chapter 2 IRS Architecture and Hardware Design
	2.1 Metamaterials: Basics of IRS
	2.2 Programmable Metasurfaces
	2.3 IRS Hardware Design
		2.3.1 IRS System Architecture
		2.3.2 IRS Element Design
		2.3.3 IRS Array Design
		2.3.4 IRS Controller Design
		2.3.5 Full‐Wave Simulation and Field Test
	2.4 State‐of‐the‐Art IRS Prototype
		2.4.1 Passive IRS Prototype by Tsinghua
		2.4.2 Active IRS Prototype by Tsinghua
		2.4.3 IRS Modulation Prototype by SEU
			2.4.3.1 Transmitter Design
			2.4.3.2 Frame Structure Design
			2.4.3.3 Receiver Design
			2.4.3.4 System Design
		2.4.4 Transmissive IRS Prototype by MIT
		2.4.5 IRS Prototype by China Mobile
		2.4.6 IRS Prototype by DOCOMO
	Bibliography
Chapter 3 On Path Loss and Channel Reciprocity of RIS‐Assisted Wireless Communications
	3.1 Introduction
	3.2 Path Loss Modeling and Channel Reciprocity Analysis
		3.2.1 System Description
		3.2.2 General Path Loss Model
		3.2.3 Path Loss Models for Typical Scenarios
		3.2.4 Discussion on RIS Path Loss and Channel Reciprocity
	3.3 Path Loss Measurement and Channel Reciprocity Validation
		3.3.1 Two Fabricated RISs
		3.3.2 Two Measurement Systems
		3.3.3 Validation of RIS Path Loss Models
		3.3.4 Validation of RIS Channel Reciprocity
	3.4 Conclusion
	3.A Appendix
		3.A.1 Proof of Theorem 3.1
	Bibliography
Chapter 4 Intelligent Surface Communication Design: Main Challenges and Solutions
	4.1 Introduction
	4.2 Channel Estimation
		4.2.1 Problem Description and Challenges
		4.2.2 Semi‐Passive IRS Channel Estimation
		4.2.3 Fully‐Passive IRS Channel Estimation
	4.3 Passive Beamforming Optimization
		4.3.1 IRS‐aided SISO System: Passive Beamforming Basics and Power Scaling Order
		4.3.2 IRS‐aided MISO System: Joint Active and Passive Beamforming
		4.3.3 IRS‐Aided MIMO System
		4.3.4 IRS‐Aided OFDM System
		4.3.5 Passive Beamforming with Discrete Reflection Amplitude and Phase Shift
		4.3.6 Other Related Works and Future Directions
	4.4 IRS Deployment
		4.4.1 IRS Deployment Optimization at the Link Level
			4.4.1.1 Optimal Deployment of Single IRS
			4.4.1.2 Single IRS versus Multiple Cooperative IRSs
			4.4.1.3 LoS versus Non‐LoS (NLoS)
		4.4.2 IRS Deployment at the Network Level: Distributed or Centralized?
		4.4.3 Other Related Work and Future Direction
	4.5 Conclusion
	Bibliography
Part II IRS for 6G Wireless Systems
Chapter 5 Overview of IRS for 6G and Industry Advance
	5.1 IRS for 6G
		5.1.1 Potential Use Cases
			5.1.1.1 Indoor Use Cases
			5.1.1.2 Outdoor Use Cases
		5.1.2 Deployment Scenarios
	5.2 Industrial Progresses
		5.2.1 Funded Projects
		5.2.2 White Papers
		5.2.3 Prototyping and Testing
		5.2.4 Standardization Progress
	Bibliography
Chapter 6 RIS‐Aided Massive MIMO Antennas*
	6.1 Introduction
		6.1.0 Notation
	6.2 System Model
		6.2.1 Channel Model
		6.2.2 Active Antenna Configuration
	6.3 Uplink/Downlink Signal Processing
		6.3.1 Uplink Channel Estimation
		6.3.2 Downlink Data Transmission
	6.4 Performance Measures
		6.4.1 SINR and Spectral Efficiency under Perfect Channel State Information (CSI)
		6.4.2 SINR and Spectral Efficiency under Imperfect Channel State Information (CSI)
			6.4.2.1 The Upper‐Bound (UB) to the System Performance
			6.4.2.2 The Hardening Lower‐Bound (LB) to System Performance
	6.5 Optimization of the RIS Phase Shifts
	6.6 Numerical Results
	6.7 Conclusions
	6.A Appendix
	Bibliography
Chapter 7 Localization, Sensing, and Their Integration with RISs
	7.1 Introduction
		7.1.1 Localization in 5G
		7.1.2 RIS Key Advantages
			7.1.2.1 Localization
			7.1.2.2 Sensing
	7.2 RIS Types and Channel Modeling
		7.2.1 RIS Hardware Architectures
		7.2.2 RIS‐Parameterized Channel Models
			7.2.2.1 Geometric Channel Model
			7.2.2.2 Stochastic Channel Modeling
	7.3 Localization with RISs
		7.3.1 Fundamentals on Localization
		7.3.2 Localization with Reflective RISs
		7.3.3 Localization with a Single STAR‐RIS
		7.3.4 Localization with Multiple Receiving RISs
	7.4 Sensing with RISs
		7.4.1 Link Budget Analysis
			7.4.1.1 Monostatic Radar Sensing
			7.4.1.2 Bistatic Radar Sensing
		7.4.2 Joint Sensing and Localization with a Single RIS
			7.4.2.0 UE and Landmark Estimates
	7.5 Conclusion and Open Challenges
	Bibliography
Chapter 8 IRS‐Aided THz Communications
	8.1 IRS‐Aided THz MIMO System Model
	8.2 Beam Training Protocol
	8.3 IRS Prototyping
		8.3.1 Active Beam Steering at THz transceivers
		8.3.2 Passive Beam Steering on THz IRS
		8.3.3 Codebook Design for Beam Scanning
		8.3.4 Beam‐Scanning Reflectarray
	8.4 IRS‐THz Communication Applications
		8.4.1 High Speed Fronthaul/Backhaul
		8.4.2 Cellular Connected Drones
		8.4.3 Wireless Data Center
		8.4.4 Enhanced Indoor Coverage
		8.4.5 Vehicular Communications
		8.4.6 Physical‐Layer Security
	Bibliography
Chapter 9 Joint Design of Beamforming, Phase Shifting, and Power Allocation in a Multi‐cluster IRS‐NOMA Network
	9.1 Introduction
		9.1.1 Previous Works
		9.1.2 Motivation and Challenge
	9.2 System Model and Problem Formulation
		9.2.1 System Model
		9.2.2 Problem Formulation
	9.3 Alternating Algorithm
		9.3.1 Beamforming Optimization
		9.3.2 Phase‐Shift Feasibility
		9.3.3 Algorithm Design
	9.4 Simulation Result
	9.5 Conclusion
	Bibliography
Chapter 10 IRS‐Aided Mobile Edge Computing: From Optimization to Learning
	10.1 Introduction
	10.2 System Model and Objective
	10.3 Optimization‐Based Approaches to IRS‐Aided MEC
		10.3.1 IRS Reflecting Coefficients Design
		10.3.2 Receive Beamforming Design
		10.3.3 Energy Partition Optimization
		10.3.4 Convergence and Complexity
	10.4 Deep Learning Approaches to IRS‐Aided MEC
		10.4.1 CSI‐Based Learning Architecture
		10.4.2 Location‐Only Learning Architecture
		10.4.3 Input Feature Uncertainty
		10.4.4 Comparison Between the CSI‐Based and CSI‐Free Learning Architectures
		10.4.5 Complexity Reduction via Learning
	10.5 Comparative Evaluation Results
		10.5.1 Scenario Without LoS Direct Links
		10.5.2 Scenario with Strong LoS Direct Links
	10.6 Conclusions
	Bibliography
Chapter 11 Interference Nulling Using Reconfigurable Intelligent Surface
	11.1 Introduction
	11.2 System Model
	11.3 Interference Nulling via RIS
		11.3.1 Feasibility of Interference Nulling
		11.3.2 Alternating Projection Algorithm
		11.3.3 Simulation Results
	11.4 Learning to Minimize Interference
		11.4.1 Learning to Initialize
		11.4.2 Simulation Results
	11.5 Conclusions
	Bibliography
Chapter 12 Blind Beamforming for IRS Without Channel Estimation
	12.1 Introduction
	12.2 System Model
	12.3 Random‐Max Sampling (RMS)
	12.4 Conditional Sample Mean (CSM)
	12.5 Some Comments on CSM
		12.5.1 Connection to Closest Point Projection
		12.5.2 Connection to Phase Retrieval
		12.5.3 CSM for General Utility Functions
	12.6 Field Tests
	12.7 Conclusion
	Bibliography
Chapter 13 RIS in Wireless Information and Power Transfer
	13.1 Introduction
		13.1.1 WPT and WIPT
		13.1.2 RIS
		13.1.3 RIS in WPT and WIPT
	13.2 RIS‐Aided WPT
		13.2.1 WPT Architecture
		13.2.2 Waveform and Beamforming
		13.2.3 Channel Acquisition
			13.2.3.1 Direct Channel
			13.2.3.2 RIS‐Related Channels
		13.2.4 Prototype and Experiments
	13.3 RIS‐Aided WIPT
		13.3.1 WIPT Categories
		13.3.2 RIS‐Aided SWIPT
			13.3.2.1 SWIPT Architecture
			13.3.2.2 Waveform and Beamforming
			13.3.2.3 Channel Acquisition
		13.3.3 RIS‐Aided WPCN and WPBC
	13.4 Conclusion
	Bibliography
Chapter 14 Beamforming Design for Self‐Sustainable IRS‐Assisted MISO Downlink Systems
	14.1 Introduction
	14.2 System Model
		14.2.1 Self‐Sustainable IRS Model
		14.2.2 Channel and Signal Models
		14.2.3 Power Harvesting Model at the IRS
	14.3 Problem Formulation
	14.4 Solution
		14.4.1 Problem Transformation
		14.4.2 Address the Coupling Variables and Binary Variables
		14.4.3 Successive Convex Approximation
	14.5 Numerical Results
	14.6 Summary
	14.7 Further Extension
	Bibliography
Chapter 15 Optical Intelligent Reflecting Surfaces
	15.1 Introduction
	15.2 System and Channel Model
		15.2.1 IRS Model
		15.2.2 Transmitter and Receiver Model
		15.2.3 Channel Model
	15.3 Communication Theoretical Modeling of Optical IRSs
		15.3.1 Scattering Theory
			15.3.1.1 Incident Beam on the IRS
			15.3.1.2 Huygens–Fresnel Principle
			15.3.1.3 Intermediate‐Field Versus Far‐Field
			15.3.1.4 Received Power Density
		15.3.2 Geometric Optics
			15.3.2.1 Equivalent Mirror‐Assisted Analysis
			15.3.2.2 Received Power Density
	15.4 Design of Optical IRSs for FSO Systems
		15.4.1 IRS‐Assisted Point‐to‐Point System
			15.4.1.1 IRS Phase‐Shift Profile Φ(r,rt)
			15.4.1.2 IRS Efficiency ζ
		15.4.2 IRS‐Assisted Multi‐Link System
			15.4.2.1 Time Division Protocol
			15.4.2.2 IRS Division Protocol
			15.4.2.3 IRS Homogenization Protocol
	15.5 Simulation Results
		15.5.1 Validation of Channel Model
		15.5.2 Performance of Multi‐Link IRS‐Assisted FSO Systems
	15.6 Future Extension
	Bibliography
Index
EULA