Intelligent Reconfigurable Surfaces (IRS) for Prospective 6G Wireless Networks
IEEE ComSoc Pocket Guides to Communications Technologies
1. Edition January 2023
304 Pages, Hardcover
Wiley & Sons Ltd
Further versions
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.
1 Introduction 1
Muhammad Ali Imran, Lina Mohjazi, Lina Bariah, Sami Muhaidat, Tei Jun Cui, and Qammer H. Abbasi
References 5
2 IRS in the Near-Field: From Basic Principles to Optimal Design 7
Konstantinos Dovelos, Stylianos D. Assimonis, Hien Q. Ngo, and Michail Matthaiou
2.1 Introduction 7
2.2 Basic Principles 8
2.2.1 IRS Model 8
2.2.2 Signal Model of IRS-Aided System 9
2.3 Near-Field Channel Model 10
2.3.1 Spherical Wavefront 10
2.3.2 Path Loss 12
2.4 Phase Shift Design 13
2.4.1 Beamfocusing 13
2.4.2 Conventional Beamforming 14
2.5 Energy Efficiency 17
2.5.1 MIMO System 17
2.5.2 IRS-aided MIMO System 18
2.6 Optimal IRS Placement 19
2.7 Open Future Research Directions 20
2.8 Conclusions 22
References 22
3 Feasibility of Intelligent Reflecting Surfaces to Combine Terrestrial and Non-terrestrial Networks 25
Muhammad A. Jamshed, Qammer H. Abbasi, and Masood Ur-Rehman
3.1 Introduction 25
3.2 Intelligent Reflecting Surfaces 27
3.2.1 Background and Architecture 27
3.2.2 Intelligent Reflecting Surfaces in Wireless Networks 28
3.3 Non-terrestrial Networks 29
3.3.1 Non-terrestrial Networks: 3GPP Vision 30
3.4 Revamping Non-terrestrial Networks Using Intelligent Reflecting Surfaces 34
3.4.1 Satellites for Communication: Background 34
3.4.2 Indoor Connectivity Using Intelligent Reflecting Surfaces 35
3.5 Conclusion 37
References 37
4 Towards the Internet of MetaMaterial Things: Software Enablers for User-Customizable Electromagnetic Wave Propagation 41
Christos Liaskos, Georgios G. Pyrialakos, Alexandros Pitilakis, Ageliki Tsioliaridou, Michail Christodoulou, Nikolaos Kantartzis, Sotiris Ioannidis, Andreas Pitsillides, and Ian F. Akyildiz
4.1 Introduction 41
4.1.1 Key Enabler 1 42
4.1.2 Key Enabler 2 43
4.2 Pre-requisites and Related Work 47
4.2.1 Meta-materials: Principles of Operation, Classification, and Supported Functionalities 49
4.3 Networked meta-materials and SDN workflows 51
4.4 Application Programming Interface for Meta-materials 53
4.4.1 Data Structures of the Meta-material API 55
4.4.2 API Callbacks and Event Handling 56
4.5 The Meta-material Middleware 58
4.5.1 Functionality Optimization Workflow: Meta-material Modelling and State Calibration 60
4.5.2 The Meta-material Functionality Profiler 64
4.6 Software Implementation and Evaluation 65
4.7 Discussion: The Transformational Potential of the IoMMT and Future Directions 73
4.8 Conclusion 75
Acknowledgements 76
References 77
5 IRS Hardware Architectures 83
Jun Y. Dai, Qiang Cheng, and Tie Jun Cui
5.1 Introduction 83
5.2 Concept, Principle, and Composition of IRS 85
5.3 Operation Mode of IRS 87
5.3.1 Prototypes of Wavefront Manipulation Mode 88
5.3.2 Prototypes of Information Modulation Mode 91
5.4 Hardware Configuration of IRS 94
5.5 Conclusions 95
References 95
6 Practical Design Considerations for Reconfigurable Intelligent Surfaces 99
James Rains, Jalil ur Rehman Kazim, Anvar Tukmanov, Lei Zhang, Qammer H. Abbasi, and Muhammad Ali Imran
6.1 Intelligent Reflecting Surface Architecture 99
6.1.1 Tunability of Unit-cell Elements 101
6.1.2 Configuration Networks 105
6.1.3 IRS Control Layer 108
6.2 Physical Limitations of IRSs 110
6.2.1 Bandwidth versus Phase Resolution 110
6.2.2 Incidence Angle Response 114
6.2.3 Quantization Effects: How Many Bits? 117
References 117
7 Channel Modelling in RIS-Empowered Wireless Communications 123
Ibrahim Yildirim and Ertugrul Basar
7.1 Introduction 123
7.2 A General Perspective on RIS Channel Modelling 125
7.3 Physical Channel Modelling for RIS-Empowered Systems at mmWave Bands 130
7.4 Physical Channel Modelling for RIS-Empowered Systems at Sub-6 GHz Bands 135
7.5 SimRIS Channel Simulator 139
7.6 Performance Analysis Using SimRIS Channel Simulator 141
7.7 Summary 145
Funding Acknowledgment 145
References 145
8 Intelligent Reflecting Surfaces (IRS)-Aided Cellular Networks and Deep Learning-Based Design 149
Taniya Shafique, Amal Feriani, Hina Tabassum, and Ekram Hossain
8.1 Introduction 149
8.2 Contributions 150
8.3 Literature Review 151
8.3.1 Optimization 151
8.3.2 Deep Learning 152
8.4 System Model 154
8.4.1 Transmission Model 154
8.4.2 IRS-Assisted Transmission 155
8.4.2.1 Desired Signal Power 155
8.4.2.2 Interference Power 156
8.4.3 Direct Transmission 157
8.4.3.1 Desired Signal Power 157
8.4.3.2 Interference Power 157
8.4.4 SINR and Achievable Rate 157
8.5 Problem Formulation 158
8.6 Phase Shifts Optimization 158
8.6.1 Optimization-based Approach 159
8.6.2 DRL-based Approach 160
8.6.2.1 Backgound 160
8.6.2.2 MDP Formulation 161
8.6.2.3 Training Procedure 161
8.6.2.4 Proximal Policy Optimization (PPO) 161
8.6.2.5 Deep Deterministic Policy Gradient (DDPG) 162
8.7 Numerical Results 163
8.7.1 Experimental Setup 163
8.7.2 Baselines 164
8.7.3 Results 164
8.8 Conclusion 167
References 167
9 Application and Future Direction of RIS 171
Jalil R. Kazim, James Rains, Muhammad Ali Imran, and Qammer H. Abbasi
9.1 Background 171
9.2 Introduction 172
9.2.1 Intelligent Reflective Surface 173
9.2.2 Analysis of RIS 174
9.2.3 Basic Functions of RIS 176
9.3 RIS-assisted High-Frequency Communication 177
9.3.1 RIS-assisted Multi-User Communication 179
9.4 RIS-assisted RF Sensing and Imaging 179
9.5 RIS-assisted-UAV Communication 180
9.6 RIS-assisted Wireless Power Transfer 181
9.7 RIS-assisted Indoor Localization 182
9.8 Conclusion 183
References 184
10 Distributed Multi-IRS-assisted 6G Wireless Networks: Channel Characterization and Performance Analysis 189
Tri N. Do, Georges Kaddoum, and Thanh L. Nguyen
10.1 Introduction 189
10.2 System Model 192
10.3 Channel Characterization and Performance Analysis 194
10.3.1 Gamma Distribution-based Statistical Channel Characterization 196
10.3.1.1 Gamma Distribution-based Ergodic Capacity Analysis 199
10.3.1.2 Gamma Distribution-based Outage Probability Analysis 200
10.3.2 Log-normal Distribution-based Statistical Channel Characterization 201
10.3.2.1 Log-normal Distribution-based Ergodic Capacity Analysis 201
10.3.2.2 Log-normal Distribution-based Outage Probability Analysis 203
10.4 Numerical Results and Discussions 203
10.5 Conclusions 209
References 210
11 RIS-Assisted UAV Communications 213
Mohammad O. Abualhauja'a, Shuja Ansari, Olaoluwa R. Popoola, Lina Mohjazi, Lina Bariah, Sami Muhaidat, Qammer H. Abbasi, and Muhammad Ali Imran
11.1 Introduction 213
11.2 Background 215
11.3 The Role of UAVs in the Future Mobile Networks and Their Unique Characteristics 216
11.3.1 UAV Characteristics 216
11.4 Challenges of UAV Communications 218
11.4.1 Air-to-Ground (3D) Channel Modelling 218
11.4.2 Three-dimensional Deployment of UAVs 219
11.4.3 Optimal Trajectory Planning 219
11.4.4 Network Planning for Cellular-connected UAV Applications 220
11.4.5 Interference Caused by Ground BSs 220
11.5 RIS-assisted UAV Communications: Integration Paradigms and Use Cases 220
11.5.1 RIS to Support UAV-assisted Communications Air-to-Ground (A2G) Links 222
11.5.2 RIS to Support Cellular-Connected UAV Ground-to-Air (G2A) Links 223
11.5.3 RIS-equipped Aerial Platforms RIS to Support Air-to-Air (A2A) Links 224
11.6 Preliminary Investigations 225
11.6.1 RIS versus Relay 225
11.6.1.1 System Model 225
11.6.1.2 Direct Transmission 226
11.6.1.3 RIS-supported Transmission 226
11.6.1.4 Relay-supported Transmission 227
11.6.1.5 Results and Discussion 227
11.7 Conclusions 229
References 229
12 Optical Wireless Communications Using Intelligent Walls 233
Anil Yesilkaya, Hanaa Abumarshoud, and Harald Haas
12.1 Introduction 233
12.2 Optical IRS: Background and Applications 235
12.2.1 IRS from the Physics Perspective 235
12.2.2 IRS Applications in OWC 238
12.2.2.1 Reflection for Blockage Mitigation 238
12.2.2.2 Enhanced Optical MIMO 240
12.2.2.3 Media-Based Modulation 241
12.2.2.4 Enhanced Optical NOMA 242
12.2.2.5 Enhanced PLS 243
12.3 Case Study: High Performance IRS-Aided Indoor LiFi 243
12.3.1 Channel Modelling 243
12.3.1.1 Generation of the Indoor Environment 245
12.3.1.2 Source Characterization 246
12.3.1.3 IRS and Coating Material Characterization 249
12.3.1.4 Receiver Characterization 252
12.3.2 Obtaining the Channel Models 254
12.3.2.1 MCRT Channel Characterization Results 256
12.3.2.2 VL Band Results 259
12.3.2.3 IR Band Results 262
12.3.3 The Achievable Rates for IRS-aided LiFi 265
12.4 Challenges and Research Directions 268
12.4.1 Modelling and Characterization 268
12.4.2 Inter-symbol Interference (ISI) 268
12.4.3 Channel Estimation 269
12.4.4 Real-time Operation 269
References 269
13 Conclusion 275
Muhammad Ali Imran, Lina Mohjazi, Lina Bariah, Sami Muhaidat, Tei Jun Cui, and Qammer H. Abbasi
Index 279
Lina Mohjazi, Lecturer, with the James Watt School of Engineering, University of Glasgow, UK.
Lina Bariah, Senior Researcher, with the Technology Innovation Institute, Abu Dhabi, UAE, and with the James Watt School of Engineering, University of Glasgow, UK.
Sami Muhaidat, Professor, with the KU Center for Cyber-Physical Systems, Khalifa University, Abu Dhabi, UAE.
Tie Jun Cui, Chief Professor of Southeast University, Nanjing, China.
Qammer H. Abbasi, Reader with the James Watt School of Engineering and Deputy Head for Communication Sensing and Imaging Group, University of Glasgow, UK.
