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Intelligent Reconfigurable Surfaces (IRS) for Prospective 6G Wireless Networks

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Intelligent Reconfigurable Surfaces (IRS) for Prospective 6G Wireless Networks

Authoritative resource covering preliminary concepts and advanced concerns in the field of IRS and its role in 6G wireless systems

Intelligent Reconfigurable Surfaces (IRS) for Prospective 6G Wireless Networks provides an in-depth treatment of the fundamental physics behind reconfigurable metasurfaces, also known as intelligent reflecting surfaces (IRS), and outlines the research roadmap towards their development as a low-complexity and energy-efficient solution aimed at turning the wireless environment into a software-defined entity.

The text demonstrates IRS from different angles, including the underlying physics, hardware architecture, operating principles, and prototype designs. It enables readers to grasp the knowledge of the interplay of IRS and state-of-the-art technologies, examining the advantages, key principles, challenges, and potential use-cases. Practically, it equips readers with the fundamental knowledge of the operational principles of reconfigurable metasurfaces, resulting in its potential applications in various intelligent, autonomous future wireless communication technologies.

To aid in reader comprehension, around 50 figures, tables, illustrations, and photographs to comprehensively present the material are also included.

Edited by a team of highly qualified professionals in the field, sample topics covered are as follows:

  • Evolution of antenna arrays design, introducing the fundamental principles of antenna theory and reviewing the stages of development of the field;
  • Beamforming design for IRS-assisted communications, discussing optimal IRS configuration in conjunction with overviewing novel beamforming designs;
  • Reconfigurable metasurfaces from physics to applications, discussing the working principles of tunable/reconfigurable metasurfaces and their capabilities and functionalities;
  • IRS hardware architectures, detailing the general hardware architecture of IRS and features related to the IRS’s main operational principle;
  • Wireless communication systems assisted by IRS, discussing channel characterization, system integration, and aspects related to the performance analysis and network optimization of state-of-the-art wireless applications.

For students and engineers in wireless communications, microwave engineering, and radio hardware and design, Intelligent Reconfigurable Surfaces (IRS) for Prospective 6G Wireless Networks serves as an invaluable resource on the subject and is a useful course accompaniment for general Antenna Theory, Microwave Engineering, Electromagnetics courses.

Author(s): Muhammad Ali Imran, Lina Mohjazi, Lina Bariah, Sami Muhaidat, Tei Jun Cui, Qammer H. Abbasi
Series: The ComSoc Guides to Communications Technologies
Publisher: Wiley-IEEE Press
Year: 2022

Language: English
Pages: 303
City: Piscataway

Cover
Title Page
Copyright
Contents
List of Contributors
Chapter 1 Introduction
References
Chapter 2 IRS in the Near‐Field: From Basic Principles to Optimal Design
2.1 Introduction
2.2 Basic Principles
2.2.1 IRS Model
2.2.2 Signal Model of IRS‐Aided System
2.3 Near‐Field Channel Model
2.3.1 Spherical Wavefront
2.3.2 Path Loss
2.4 Phase Shift Design
2.4.1 Beamfocusing
2.4.2 Conventional Beamforming
2.5 Energy Efficiency
2.5.1 MIMO System
2.5.2 IRS‐aided MIMO System
2.6 Optimal IRS Placement
2.7 Open Future Research Directions
2.8 Conclusions
References
Chapter 3 Feasibility of Intelligent Reflecting Surfaces to Combine Terrestrial and Non‐terrestrial Networks
3.1 Introduction
3.2 Intelligent Reflecting Surfaces
3.2.1 Background and Architecture
3.2.2 Intelligent Reflecting Surfaces in Wireless Networks
3.3 Non‐terrestrial Networks
3.3.1 Non‐terrestrial Networks: 3GPP Vision
3.4 Revamping Non‐terrestrial Networks Using Intelligent Reflecting Surfaces
3.4.1 Satellites for Communication: Background
3.4.2 Indoor Connectivity Using Intelligent Reflecting Surfaces
3.5 Conclusion
References
Chapter 4 Towards the Internet of MetaMaterial Things: Software Enablers for User‐Customizable Electromagnetic Wave Propagation
4.1 Introduction
4.1.1 Key Enabler 1
4.1.2 Key Enabler 2
4.2 Pre‐requisites and Related Work
4.2.1 Meta‐materials: Principles of Operation, Classification, and Supported Functionalities
4.3 Networked meta‐materials and SDN workflows
4.4 Application Programming Interface for Meta‐materials
4.4.1 Data Structures of the Meta‐material API
4.4.2 API Callbacks and Event Handling
4.5 The Meta‐material Middleware
4.5.1 Functionality Optimization Workflow: Meta‐material Modelling and State Calibration
4.5.2 The Meta‐material Functionality Profiler
4.6 Software Implementation and Evaluation
4.7 Discussion: The Transformational Potential of the IoMMT and Future Directions
4.8 Conclusion
Acknowledgements
References
Chapter 5 IRS Hardware Architectures
5.1 Introduction
5.2 Concept, Principle, and Composition of IRS
5.3 Operation Mode of IRS
5.3.1 Prototypes of Wavefront Manipulation Mode
5.3.2 Prototypes of Information Modulation Mode
5.4 Hardware Configuration of IRS
5.5 Conclusions
References
Chapter 6 Practical Design Considerations for Reconfigurable Intelligent Surfaces
6.1 Intelligent Reflecting Surface Architecture
6.1.1 Tunability of Unit‐cell Elements
6.1.2 Configuration Networks
6.1.3 IRS Control Layer
6.2 Physical Limitations of IRSs
6.2.1 Bandwidth versus Phase Resolution
6.2.2 Incidence Angle Response
6.2.3 Quantization Effects: How Many Bits?
References
Chapter 7 Channel Modelling in RIS‐Empowered Wireless Communications
7.1 Introduction
7.2 A General Perspective on RIS Channel Modelling
7.3 Physical Channel Modelling for RIS‐Empowered Systems at mmWave Bands
7.4 Physical Channel Modelling for RIS‐Empowered Systems at Sub‐6 GHz Bands
7.5 SimRIS Channel Simulator
7.6 Performance Analysis Using SimRIS Channel Simulator
7.7 Summary
Funding Acknowledgment
References
Chapter 8 Intelligent Reflecting Surfaces (IRS)‐Aided Cellular Networks and Deep Learning‐Based Design
8.1 Introduction
8.2 Contributions
8.3 Literature Review
8.3.1 Optimization
8.3.2 Deep Learning
8.4 System Model
8.4.1 Transmission Model
8.4.2 IRS‐Assisted Transmission
8.4.2.1 Desired Signal Power
8.4.2.2 Interference Power
8.4.3 Direct Transmission
8.4.3.1 Desired Signal Power
8.4.3.2 Interference Power
8.4.4 SINR and Achievable Rate
8.5 Problem Formulation
8.6 Phase Shifts Optimization
8.6.1 Optimization‐based Approach
8.6.2 DRL‐based Approach
8.6.2.1 Backgound
8.6.2.2 MDP Formulation
8.6.2.3 Training Procedure
8.6.2.4 Proximal Policy Optimization (PPO)
8.6.2.5 Deep Deterministic Policy Gradient (DDPG)
8.7 Numerical Results
8.7.1 Experimental Setup
8.7.2 Baselines
8.7.3 Results
8.8 Conclusion
References
Chapter 9 Application and Future Direction of RIS
9.1 Background
9.2 Introduction
9.2.1 Intelligent Reflective Surface
9.2.2 Analysis of RIS
9.2.3 Basic Functions of RIS
9.3 RIS‐assisted High‐Frequency Communication
9.3.1 RIS‐assisted Multi‐User Communication
9.4 RIS‐assisted RF Sensing and Imaging
9.5 RIS‐assisted‐UAV Communication
9.6 RIS‐assisted Wireless Power Transfer
9.7 RIS‐assisted Indoor Localization
9.8 Conclusion
References
Chapter 10 Distributed Multi‐IRS‐assisted 6G Wireless Networks: Channel Characterization and Performance Analysis
10.1 Introduction
10.2 System Model
10.3 Channel Characterization and Performance Analysis
10.3.1 Gamma Distribution‐based Statistical Channel Characterization
10.3.1.1 Gamma Distribution‐based Ergodic Capacity Analysis
10.3.1.2 Gamma Distribution‐based Outage Probability Analysis
10.3.2 Log‐normal Distribution‐based Statistical Channel Characterization
10.3.2.1 Log‐normal Distribution‐based Ergodic Capacity Analysis
10.3.2.2 Log‐normal Distribution‐based Outage Probability Analysis
10.4 Numerical Results and Discussions
10.5 Conclusions
References
Chapter 11 RIS‐Assisted UAV Communications
11.1 Introduction
11.2 Background
11.3 The Role of UAVs in the Future Mobile Networks and Their Unique Characteristics
11.3.1 UAV Characteristics
11.4 Challenges of UAV Communications
11.4.1 Air‐to‐Ground (3D) Channel Modelling
11.4.2 Three‐dimensional Deployment of UAVs
11.4.3 Optimal Trajectory Planning
11.4.4 Network Planning for Cellular‐connected UAV Applications
11.4.5 Interference Caused by Ground BSs
11.5 RIS‐assisted UAV Communications: Integration Paradigms and Use Cases
11.5.1 RIS to Support UAV‐assisted Communications Air‐to‐Ground (A2G) Links
11.5.2 RIS to Support Cellular‐Connected UAV Ground‐to‐Air (G2A) Links
11.5.3 RIS‐equipped Aerial Platforms RIS to Support Air‐to‐Air (A2A) Links
11.6 Preliminary Investigations
11.6.1 RIS versus Relay
11.6.1.1 System Model
11.6.1.2 Direct Transmission
11.6.1.3 RIS‐supported Transmission
11.6.1.4 Relay‐supported Transmission
11.6.1.5 Results and Discussion
11.7 Conclusions
References
Chapter 12 Optical Wireless Communications Using Intelligent Walls*
12.1 Introduction
12.2 Optical IRS: Background and Applications
12.2.1 IRS from the Physics Perspective
12.2.2 IRS Applications in OWC
12.2.2.1 Reflection for Blockage Mitigation
12.2.2.2 Enhanced Optical MIMO
12.2.2.3 Media‐Based Modulation
12.2.2.4 Enhanced Optical NOMA
12.2.2.5 Enhanced PLS
12.3 Case Study: High Performance IRS‐Aided Indoor LiFi
12.3.1 Channel Modelling
12.3.1.1 Generation of the Indoor Environment
12.3.1.2 Source Characterization
12.3.1.3 IRS and Coating Material Characterization
12.3.1.4 Receiver Characterization
12.3.2 Obtaining the Channel Models
12.3.2.1 MCRT Channel Characterization Results
12.3.2.2 VL Band Results
12.3.2.3 IR Band Results
12.3.3 The Achievable Rates for IRS‐aided LiFi
12.4 Challenges and Research Directions
12.4.1 Modelling and Characterization
12.4.2 Inter‐symbol Interference (ISI)
12.4.3 Channel Estimation
12.4.4 Real‐time Operation
References
Chapter 13 Conclusion
Index
EULA