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

Muhammad Ali Imran, Professor of Communication Systems in University of Glasgow, Dean University of Glasgow UESTC and Head of Communications, Sensing and Imaging Group.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.

• List of Contributors xiii1 Introduction 1Muhammad Ali Imran, Lina Mohjazi, Lina Bariah, Sami Muhaidat, Tei Jun Cui, and Qammer H. AbbasiReferences 52 IRS in the Near-Field: From Basic Principles to Optimal Design 7Konstantinos Dovelos, Stylianos D. Assimonis, Hien Q. Ngo, and Michail Matthaiou2.1 Introduction 72.2 Basic Principles 82.2.1 IRS Model 82.2.2 Signal Model of IRS-Aided System 92.3 Near-Field Channel Model 102.3.1 Spherical Wavefront 102.3.2 Path Loss 122.4 Phase Shift Design 132.4.1 Beamfocusing 132.4.2 Conventional Beamforming 142.5 Energy Efficiency 172.5.1 MIMO System 172.5.2 IRS-aided MIMO System 182.6 Optimal IRS Placement 192.7 Open Future Research Directions 202.8 Conclusions 22References 223 Feasibility of Intelligent Reflecting Surfaces to Combine Terrestrial and Non-terrestrial Networks 25Muhammad A. Jamshed, Qammer H. Abbasi, and Masood Ur-Rehman3.1 Introduction 253.2 Intelligent Reflecting Surfaces 273.2.1 Background and Architecture 273.2.2 Intelligent Reflecting Surfaces in Wireless Networks 283.3 Non-terrestrial Networks 293.3.1 Non-terrestrial Networks: 3GPP Vision 303.4 Revamping Non-terrestrial Networks Using Intelligent Reflecting Surfaces 343.4.1 Satellites for Communication: Background 343.4.2 Indoor Connectivity Using Intelligent Reflecting Surfaces 353.5 Conclusion 37References 374 Towards the Internet of MetaMaterial Things: Software Enablers for User-Customizable Electromagnetic Wave Propagation 41Christos Liaskos, Georgios G. Pyrialakos, Alexandros Pitilakis, Ageliki Tsioliaridou, Michail Christodoulou, Nikolaos Kantartzis, Sotiris Ioannidis, Andreas Pitsillides, and Ian F. Akyildiz4.1 Introduction 414.1.1 Key Enabler 1 424.1.2 Key Enabler 2 434.2 Pre-requisites and Related Work 474.2.1 Meta-materials: Principles of Operation, Classification, and Supported Functionalities 494.3 Networked meta-materials and SDN workflows 514.4 Application Programming Interface for Meta-materials 534.4.1 Data Structures of the Meta-material API 554.4.2 API Callbacks and Event Handling 564.5 The Meta-material Middleware 584.5.1 Functionality Optimization Workflow: Meta-material Modelling and State Calibration 604.5.2 The Meta-material Functionality Profiler 644.6 Software Implementation and Evaluation 654.7 Discussion: The Transformational Potential of the IoMMT and Future Directions 734.8 Conclusion 75Acknowledgements 76References 775 IRS Hardware Architectures 83Jun Y. Dai, Qiang Cheng, and Tie Jun Cui5.1 Introduction 835.2 Concept, Principle, and Composition of IRS 855.3 Operation Mode of IRS 875.3.1 Prototypes of Wavefront Manipulation Mode 885.3.2 Prototypes of Information Modulation Mode 915.4 Hardware Configuration of IRS 945.5 Conclusions 95References 956 Practical Design Considerations for Reconfigurable Intelligent Surfaces 99James Rains, Jalil ur Rehman Kazim, Anvar Tukmanov, Lei Zhang, Qammer H. Abbasi, and Muhammad Ali Imran6.1 Intelligent Reflecting Surface Architecture 996.1.1 Tunability of Unit-cell Elements 1016.1.2 Configuration Networks 1056.1.3 IRS Control Layer 1086.2 Physical Limitations of IRSs 1106.2.1 Bandwidth versus Phase Resolution 1106.2.2 Incidence Angle Response 1146.2.3 Quantization Effects: How Many Bits? 117References 1177 Channel Modelling in RIS-Empowered Wireless Communications 123Ibrahim Yildirim and Ertugrul Basar7.1 Introduction 1237.2 A General Perspective on RIS Channel Modelling 1257.3 Physical Channel Modelling for RIS-Empowered Systems at mmWave Bands 1307.4 Physical Channel Modelling for RIS-Empowered Systems at Sub-6 GHz Bands 1357.5 SimRIS Channel Simulator 1397.6 Performance Analysis Using SimRIS Channel Simulator 1417.7 Summary 145Funding Acknowledgment 145References 1458 Intelligent Reflecting Surfaces (IRS)-Aided Cellular Networks and Deep Learning-Based Design 149Taniya Shafique, Amal Feriani, Hina Tabassum, and Ekram Hossain8.1 Introduction 1498.2 Contributions 1508.3 Literature Review 1518.3.1 Optimization 1518.3.2 Deep Learning 1528.4 System Model 1548.4.1 Transmission Model 1548.4.2 IRS-Assisted Transmission 1558.4.2.1 Desired Signal Power 1558.4.2.2 Interference Power 1568.4.3 Direct Transmission 1578.4.3.1 Desired Signal Power 1578.4.3.2 Interference Power 1578.4.4 SINR and Achievable Rate 1578.5 Problem Formulation 1588.6 Phase Shifts Optimization 1588.6.1 Optimization-based Approach 1598.6.2 DRL-based Approach 1608.6.2.1 Backgound 1608.6.2.2 MDP Formulation 1618.6.2.3 Training Procedure 1618.6.2.4 Proximal Policy Optimization (PPO) 1618.6.2.5 Deep Deterministic Policy Gradient (DDPG) 1628.7 Numerical Results 1638.7.1 Experimental Setup 1638.7.2 Baselines 1648.7.3 Results 1648.8 Conclusion 167References 1679 Application and Future Direction of RIS 171Jalil R. Kazim, James Rains, Muhammad Ali Imran, and Qammer H. Abbasi9.1 Background 1719.2 Introduction 1729.2.1 Intelligent Reflective Surface 1739.2.2 Analysis of RIS 1749.2.3 Basic Functions of RIS 1769.3 RIS-assisted High-Frequency Communication 1779.3.1 RIS-assisted Multi-User Communication 1799.4 RIS-assisted RF Sensing and Imaging 1799.5 RIS-assisted-UAV Communication 1809.6 RIS-assisted Wireless Power Transfer 1819.7 RIS-assisted Indoor Localization 1829.8 Conclusion 183References 18410 Distributed Multi-IRS-assisted 6G Wireless Networks: Channel Characterization and Performance Analysis 189Tri N. Do, Georges Kaddoum, and Thanh L. Nguyen10.1 Introduction 18910.2 System Model 19210.3 Channel Characterization and Performance Analysis 19410.3.1 Gamma Distribution-based Statistical Channel Characterization 19610.3.1.1 Gamma Distribution-based Ergodic Capacity Analysis 19910.3.1.2 Gamma Distribution-based Outage Probability Analysis 20010.3.2 Log-normal Distribution-based Statistical Channel Characterization 20110.3.2.1 Log-normal Distribution-based Ergodic Capacity Analysis 20110.3.2.2 Log-normal Distribution-based Outage Probability Analysis 20310.4 Numerical Results and Discussions 20310.5 Conclusions 209References 21011 RIS-Assisted UAV Communications 213Mohammad O. Abualhauja’a, Shuja Ansari, Olaoluwa R. Popoola, Lina Mohjazi, Lina Bariah, Sami Muhaidat, Qammer H. Abbasi, and Muhammad Ali Imran11.1 Introduction 21311.2 Background 21511.3 The Role of UAVs in the Future Mobile Networks and Their Unique Characteristics 21611.3.1 UAV Characteristics 21611.4 Challenges of UAV Communications 21811.4.1 Air-to-Ground (3D) Channel Modelling 21811.4.2 Three-dimensional Deployment of UAVs 21911.4.3 Optimal Trajectory Planning 21911.4.4 Network Planning for Cellular-connected UAV Applications 22011.4.5 Interference Caused by Ground BSs 22011.5 RIS-assisted UAV Communications: Integration Paradigms and Use Cases 22011.5.1 RIS to Support UAV-assisted Communications Air-to-Ground (A2G) Links 22211.5.2 RIS to Support Cellular-Connected UAV Ground-to-Air (G2A) Links 22311.5.3 RIS-equipped Aerial Platforms RIS to Support Air-to-Air (A2A) Links 22411.6 Preliminary Investigations 22511.6.1 RIS versus Relay 22511.6.1.1 System Model 22511.6.1.2 Direct Transmission 22611.6.1.3 RIS-supported Transmission 22611.6.1.4 Relay-supported Transmission 22711.6.1.5 Results and Discussion 22711.7 Conclusions 229References 22912 Optical Wireless Communications Using Intelligent Walls 233Anil Yesilkaya, Hanaa Abumarshoud, and Harald Haas12.1 Introduction 23312.2 Optical IRS: Background and Applications 23512.2.1 IRS from the Physics Perspective 23512.2.2 IRS Applications in OWC 23812.2.2.1 Reflection for Blockage Mitigation 23812.2.2.2 Enhanced Optical MIMO 24012.2.2.3 Media-Based Modulation 24112.2.2.4 Enhanced Optical NOMA 24212.2.2.5 Enhanced PLS 24312.3 Case Study: High Performance IRS-Aided Indoor LiFi 24312.3.1 Channel Modelling 24312.3.1.1 Generation of the Indoor Environment 24512.3.1.2 Source Characterization 24612.3.1.3 IRS and Coating Material Characterization 24912.3.1.4 Receiver Characterization 25212.3.2 Obtaining the Channel Models 25412.3.2.1 MCRT Channel Characterization Results 25612.3.2.2 VL Band Results 25912.3.2.3 IR Band Results 26212.3.3 The Achievable Rates for IRS-aided LiFi 26512.4 Challenges and Research Directions 26812.4.1 Modelling and Characterization 26812.4.2 Inter-symbol Interference (ISI) 26812.4.3 Channel Estimation 26912.4.4 Real-time Operation 269References 26913 Conclusion 275Muhammad Ali Imran, Lina Mohjazi, Lina Bariah, Sami Muhaidat, Tei Jun Cui, and Qammer H. AbbasiIndex 279