Reconfigurable Intelligent Surface-Empowered 6G (Wireless Networks)

✍ Scribed by Hongliang Zhang, Boya Di, Lingyang Song, Zhu Han

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 260
• Edition: 1st ed. 2021
• Category: Library

✦ Synopsis

This book presents novel RIS-Based Smart Radio techniques, targeting at achieving high-quality channel links in cellular communications via design and optimization of the RIS construction. Unlike traditional antenna arrays, three unique characteristics of  the RIS will be revealed in this book. First, the built-in programmable configuration of the RIS enables analog beamforming inherently without extra hardware or signal processing. Second, the incident signals can be controlled to partly reflect and partly transmit through the RIS simultaneously, adding more flexibility to signal transmission. Third, the RIS has no digital processing capability to actively send signals nor any radio frequency (RF) components. As such, it is necessary to develop novel channel estimation and communication protocols, design joint digital and RIS-based analog beamforming schemes and perform interference control via mixed reflection and transmission. This book also investigates how to integrate the RIS to legacy communication systems. 

RIS techniques are further investigated in this book (benefited from its ability to actively shape the propagation environment) to achieve two types of wireless applications, i.e., RF sensing and localization. The influence of the sensing objectives on the wireless signal propagation can be potentially recognized by the receivers, which are then utilized to identify the objectives in RF sensing. Unlike traditional sensing techniques, RIS-aided sensing can actively customize the wireless channels and generate a favorable massive number of independent paths interacting with the sensing objectives. It is desirable to design RIS-based sensing algorithms, and optimize RIS configurations. For the second application, i.e., RIS aided localization, an RIS is deployed between the access point (AP) and users. The AP can then analyze reflected signals from users via different RIS configurations to obtain accurate locations of users. However, this is a challenging task due to the dynamic user topology, as well as the mutual influence between multiple users and the RIS. Therefore, the operations of the RIS, the AP, and multiple users need to be carefully coordinated. A new RIS-based localization protocol for device cooperation and an RIS configuration optimization algorithm are also required. 

This book targets researchers and graduate-level students focusing on communications and networks. Signal processing engineers, computer and information scientists, applied mathematicians and statisticians, who work in RIS research and development will also find this book useful.

✦ Table of Contents

Preface
Contents
Acronyms
1 Introductions and Basics
1.1 Background and Requirements
1.2 Overview of RISs
1.2.1 History of Meta-Materials
1.2.2 Working Principles
1.2.3 Applications of RISs
1.2.3.1 Wireless Communications
1.2.3.2 RF Sensing
1.3 Fundamentals of RIS-Aided Wireless Communications
1.3.1 Response Model
1.3.1.1 Reflective Type
1.3.1.2 Transmissive Type
1.3.1.3 Hybrid Type
1.3.2 Channel Model
1.3.2.1 Large-Scale Path Loss Model
1.3.2.2 Dyadic Backscatter Channel Model
1.3.2.3 Spatial Scattering Channel Model
1.3.3 Comparisons with Existing Techniques
1.3.3.1 RIS vs. Massive MIMO
1.3.3.2 RIS vs. Relay
1.3.3.3 RIS vs. Backscatter
References
2 RIS Aided MIMO Communications
2.1 Limited Phase Shifts: How Many Phase Shifts Are Enough?
2.1.1 Motivations
2.1.2 System Model
2.1.2.1 RIS Assisted Communication Model
2.1.2.2 Reflection Dominant Channel Model
2.1.3 Achievable Data Rate Analysis
2.1.4 Analysis on the Number of Phase Shifts
2.1.5 Simulation Results
2.1.6 Summary
2.1.7 Appendix
2.2 Size Effect: How Many Reflective Elements Do We Need?
2.2.1 Motivation
2.2.2 System Model
2.2.2.1 Scenario Description
2.2.2.2 Channel Model
2.2.2.3 Achievable Rate with Zero-Forcing Precoding
2.2.3 Analysis on Asymptotic Capacity
2.2.4 Analysis on the Number of Reflective Elements
2.2.4.1 Lower Bound of ε
2.2.4.2 Solution of Problem (2.51)
2.2.5 Simulation Results
2.2.6 Summary
2.3 Coverage Extension: RIS Orientation and Location Optimization
2.3.1 Motivations
2.3.2 System Model
2.3.2.1 Scenario Description
2.3.2.2 Channel Model
2.3.3 Cell Coverage Analysis
2.3.3.1 Optimal Phase Shifts of RIS
2.3.3.2 Cell Coverage Analysis
2.3.4 RIS Placement Optimization
2.3.4.1 Coverage Maximization Problem Formulation
2.3.4.2 Coverage Maximization Algorithm Design
2.3.5 Simulation Results
2.3.6 Summary
2.4 Hybrid Beamforming Design
2.4.1 Motivations
2.4.2 System Model
2.4.2.1 Scenario Description
2.4.2.2 Reconfigurable Intelligent Surface with Limited Discrete Phase Shifts
2.4.2.3 Reflection-Dominated Channel Model
2.4.3 RIS-Based Hybrid Beamforming and Problem Formulation for Multi-User Communications
2.4.3.1 Hybrid Beamforming Scheme
2.4.3.2 Sum Rate Maximization Problem Formulation
2.4.3.3 Problem Decomposition
2.4.4 Sum Rate Maximization Algorithm Design
2.4.4.1 Digital Beamforming Algorithm
2.4.4.2 RIS Configuration Based Analog Beamforming Algorithm
2.4.4.3 Overall Algorithm Description
2.4.4.4 Convergence and Complexity Analysis
2.4.5 Performance Analysis of RIS-Based Multi-User Communications
2.4.5.1 Comparison with Traditional Hybrid Beamforming
2.4.5.2 Special Case: Pure Line-of-Sight Transmissions
2.4.6 Simulation Results
2.4.7 Summary
2.5 Full-Dimensional Coverage Extension
2.5.1 Motivations
2.5.2 Intelligent Omni-Surface
2.5.3 System Model
2.5.3.1 Scenario Description
2.5.3.2 Channel Model
2.5.3.3 IOS-Based Beamforming
2.5.4 Problem Formulation and Decomposition
2.5.4.1 Problem Formulation
2.5.4.2 Problem Decomposition
2.5.5 Sum-Rate Maximization: Algorithm Design
2.5.5.1 Digital Beamforming Optimization at the SBS
2.5.5.2 Analog Beamforming Optimization at the IOS
2.5.5.3 Joint SBS Digital Beamforming and IOS Phase Shift Optimization
2.5.6 Performance Analysis of the IOS-Assisted Communication System
2.5.6.1 Analysis of the Phase Shift Design
2.5.6.2 Analysis of the Transmission/Reflection Power Ratio
2.5.7 Simulation Results
2.5.8 Summary
References
3 Convergences of RISs with Existing Wireless Technologies
3.1 RIS Aided Device-to-Device Communications
3.1.1 Motivations
3.1.2 System Model
3.1.2.1 System Description
3.1.2.2 Interference Analysis
3.1.3 Problem Formulation
3.1.3.1 Sum Rate Maximization Problem Formulation
3.1.3.2 Problem Decomposition
3.1.4 Sum Rate Maximization Algorithm
3.1.4.1 Power Allocation Sub-problem Algorithm Design
3.1.4.2 Discrete Phase Shift Optimization Sub-problem Algorithm Design
3.1.4.3 Sum Rate Maximization
3.1.4.4 Convergence, Feasibility and Complexity Analysis
3.1.5 Performance Evaluation
3.1.5.1 Simulation Setup
3.1.5.2 Performance Evaluation
3.1.6 Summary
3.2 RIS Aided Cell-Free MIMO
3.2.1 Motivations
3.2.2 System Model
3.2.2.1 Scenario Description
3.2.2.2 RIS Reflection Model
3.2.2.3 Channel Model
3.2.3 Hybrid Beamforming and Problem Formulation
3.2.3.1 Hybrid Beamforming Scheme
3.2.3.2 Energy Efficiency Maximization Problem Formulation
3.2.3.3 Problem Decomposition
3.2.4 Energy Efficiency Maximization Algorithm Design
3.2.4.1 Digital Beamforming Design
3.2.4.2 RIS-Based Analog Beamforming Design
3.2.4.3 Overall Algorithm Description
3.2.5 Theoretical Analysis of RIS Aided Cell-Free System
3.2.5.1 Properties of the Energy Efficiency Maximization Algorithm
3.2.5.2 Performance Analysis of RIS Aided Cell-Free System
3.2.6 Simulation Results
3.2.7 Summary
3.2.8 Appendix
3.3 RIS Aided Spatial Equalization
3.3.1 Motivations
3.3.2 System Model
3.3.3 Problem Formulation
3.3.4 Algorithm Design
3.3.5 Simulation Results
3.3.6 Summary
3.3.7 Appendix
References
4 RIS Aided RF Sensing and Localization
4.1 2D Sensing
4.1.1 Motivations
4.1.2 System Design
4.1.2.1 RIS Model
4.1.2.2 Channel Model
4.1.2.3 Protocol Design
4.1.3 Problem Formulation of RIS-Based Posture Recognition
4.1.3.1 Problem Formulation
4.1.3.2 Problem Decomposition
4.1.4 Algorithms for Configuration Matrix and Decision Function Optimizations
4.1.4.1 Configuration Optimization Algorithm
4.1.4.2 Supervised Learning Algorithm for Solving (P4.3)
4.1.5 Performance Analysis
4.1.5.1 Convergence of FCAO Algorithm
4.1.5.2 Convergence of Supervised Learning Algorithm
4.1.5.3 Optimality of Decision Function
4.1.5.4 Upper-Bound on Minimal Average False Recognition Cost
4.1.6 System Implementation
4.1.6.1 Implementation of RIS
4.1.6.2 Implementation of Transceiver Module
4.1.7 Simulation and Experimental Results
4.1.7.1 System Setting for Simulation and Experiment
4.1.7.2 Simulation Results
4.1.7.3 Experimental Results
4.1.8 Summary
4.1.9 Appendix
4.2 3D Sensing
4.2.1 Motivations
4.2.2 System Model
4.2.2.1 RIS Model
4.2.2.2 Channel Model
4.2.2.3 RF Sensing Protocol
4.2.3 Problem Formulation
4.2.4 Algorithm Design
4.2.4.1 MDP Formulation
4.2.4.2 Progressing Reward Policy Gradient Algorithm
4.2.5 Algorithm Analysis
4.2.5.1 Computational Complexity
4.2.5.2 Convergence Analysis
4.2.5.3 Lower Bound for Sensing Accuracy
4.2.6 Simulation and Evaluation
4.2.6.1 Simulation Settings
4.2.6.2 Results
4.2.7 Summary
4.3 Indoor Localization
4.3.1 Motivations
4.3.2 Related Work
4.3.3 System Overview
4.3.4 Radio Map Preparation Phase
4.3.4.1 Building an RIS
4.3.4.2 Changing the RSS Value at a Location
4.3.4.3 RSS Modeling
4.3.4.4 Compressive Construction Technique
4.3.5 Fine-Grained Localization Phase
4.3.5.1 Soft Localization
4.3.5.2 RIS Configuration Selection
4.3.5.3 Termination of the Localization Phase
4.3.6 Implementation
4.3.6.1 RIS Module
4.3.6.2 Access Point and User Modules
4.3.6.3 Workflow Setting
4.3.7 Evaluation
4.3.7.1 Experimental Setup
4.3.7.2 Results for Radio Map Construction
4.3.7.3 Results for Single User Localization
4.3.7.4 Results for Multiple User Localization Without Obstruction
4.3.7.5 Results for Multiple User Localization with Obstruction
4.3.8 Discussion
4.3.9 Summary
4.3.10 Appendix
References