Intelligent Surfaces Empowered 6G Wireless Network

✍ Scribed by Qingqing Wu, Trung Q. Duong, Derrick Wing Kwan Ng, Robert Schober

• Publisher: Wiley
• Year: 2023
• Tongue: English
• Leaves: 365
• Category: Library

✦ Synopsis

The next generation of wireless technology (6G) promises to transform wireless communication and human interconnectivity like never before. Intelligent surface, which adopts significant numbers of small reflective surfaces to reconfigure wireless connections and improve network performance, has recently been recognized as a critical component for enabling future 6G. The next phase of wireless technology demands engineers and researchers are familiar with this technology and are able to cope with the challenges.

Intelligent Surfaces Empowered 6G Wireless Network provides a thorough overview of intelligent surface technologies and their applications in wireless networks and 6G. It includes an introduction to the fundamentals of intelligent surfaces, before moving to more advanced content for engineers who understand them and look to apply them in the 6G realm. Its detailed discussion of the challenges and opportunities posed by intelligent surfaces empowered wireless networks makes it the first work of its kind.

Intelligent Surfaces Empowered 6G Wireless Network readers will also find:

An editorial team including the original pioneers of intelligent surface technology.
Detailed coverage of subjects including MIMO, terahertz, NOMA, energy harvesting, physical layer security, computing, sensing, machine learning, and more.
Discussion of hardware design, signal processing techniques, and other critical aspects of IRS engineering.

Intelligent Surfaces Empowered 6G Wireless Network is a must for students, researchers, and working engineers looking to understand this vital aspect of the coming 6G revolution.

✦ Table of Contents

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