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<p>About the Editors xiii</p> <p>List of Contributors xv</p> <p>Preface xxi</p> <p>Acknowledgement xxiii</p> <p><b>Part I Fundamentals of IRS 1</b></p> <p><b>1 Introduction to Intelligent Surfaces 3</b><br /><i>Kaitao Meng, Qingqing Wu, Trung Q. Duong, Derrick Wing Kwan Ng, Robert Schober, and Rui Zhang</i></p> <p>1.1 Background 3</p> <p>1.2 Concept of Intelligent Surfaces 5</p> <p>1.3 Advantages of Intelligence Surface 7</p> <p>1.4 Potential Applications 8</p> <p>1.5 Conclusion 12</p> <p><b>2 IRS Architecture and Hardware Design 15</b><br /><i>Zijian Zhang, Yuhao Chen, Qiumo Yu, and Linglong Dai</i></p> <p>2.1 Metamaterials: Basics of IRS 15</p> <p>2.2 Programmable Metasurfaces 16</p> <p>2.3 IRS Hardware Design 18</p> <p>2.4 State-of-the-Art IRS Prototype 23</p> <p>3 On Path Loss and Channel Reciprocity of RIS-Assisted Wireless Communications 37</p> <p>Wankai Tang, Jinghe Wang, Jun Yan Dai, Marco Di Renzo, Shi Jin, Qiang Cheng, and Tie Jun Cui</p> <p>3.1 Introduction 37</p> <p>3.2 Path Loss Modeling and Channel Reciprocity Analysis 39</p> <p>3.3 Path Loss Measurement and Channel Reciprocity Validation 47</p> <p>3.4 Conclusion 54</p> <p><b>4 Intelligent Surface Communication Design: Main Challenges and Solutions 59</b><br /><i>Kaitao Meng, Qingqing Wu, and Rui Zhang</i></p> <p>4.1 Introduction 59</p> <p>4.2 Channel Estimation 59</p> <p>4.3 Passive Beamforming Optimization 65</p> <p>4.4 IRS Deployment 73</p> <p>4.5 Conclusion 79</p> <p><b>Part II IRS for 6G Wireless Systems 83</b></p> <p><b>5 Overview of IRS for 6G and Industry Advance 85</b><br /><i>Ruiqi (Richie) Liu, Konstantinos D. Katsanos, Qingqing Wu, and George C. Alexandropoulos</i></p> <p>5.1 IRS for 6G 85</p> <p>5.2 Industrial Progresses 98</p> <p><b>6 RIS-Aided Massive MIMO Antennas 117</b><br /><i>Stefano Buzzi, Carmen D'Andrea, and Giovanni Interdonato</i></p> <p>6.1 Introduction 117</p> <p>6.2 System Model 119</p> <p>6.3 Uplink/Downlink Signal Processing 123</p> <p>6.4 Performance Measures 126</p> <p>6.5 Optimization of the RIS Phase Shifts 128</p> <p>6.6 Numerical Results 130</p> <p>6.7 Conclusions 134</p> <p><b>7 Localization, Sensing, and Their Integration with RISs 139</b><br /><i>George C. Alexandropoulos, Hyowon Kim, Jiguang He, and Henk Wymeersch</i></p> <p>7.1 Introduction 139</p> <p>7.2 RIS Types and Channel Modeling 142</p> <p>7.3 Localization with RISs 147</p> <p>7.4 Sensing with RISs 154</p> <p>7.5 Conclusion and Open Challenges 159</p> <p><b>8 IRS-Aided THz Communications 167</b><br /><i>Boyu Ning and Zhi Chen</i></p> <p>8.1 IRS-Aided THz MIMO System Model 167</p> <p>8.2 Beam Training Protocol 168</p> <p>8.3 IRS Prototyping 175</p> <p>8.4 IRS-THz Communication Applications 182</p> <p><b>9 Joint Design of Beamforming, Phase Shifting, and Power Allocation in a Multi-cluster IRS-NOMA Network 187</b><br /><i>Ximing Xie, Fang Fang, and Zhiguo Ding</i></p> <p>9.1 Introduction 187</p> <p>9.2 System Model and Problem Formulation 190</p> <p>9.3 Alternating Algorithm 193</p> <p>9.4 Simulation Result 200</p> <p>9.5 Conclusion 203</p> <p><b>10 IRS-Aided Mobile Edge Computing: From Optimization to Learning 207</b><br /><i>Xiaoyan Hu, Kai-Kit Wong, Christos Masouros, and Shi Jin</i></p> <p>10.1 Introduction 207</p> <p>10.2 System Model and Objective 208</p> <p>10.3 Optimization-Based Approaches to IRS-Aided MEC 211</p> <p>10.4 Deep Learning Approaches to IRS-Aided MEC 216</p> <p>10.5 Comparative Evaluation Results 222</p> <p>10.6 Conclusions 226</p> <p><b>11 Interference Nulling Using Reconfigurable Intelligent Surface 229</b><br /><i>Tao Jiang, Foad Sohrabi, and Wei Yu</i></p> <p>11.1 Introduction 229</p> <p>11.2 System Model 231</p> <p>11.3 Interference Nulling via RIS 232</p> <p>11.4 Learning to Minimize Interference 241</p> <p>11.5 Conclusions 247</p> <p><b>12 Blind Beamforming for IRS Without Channel Estimation 251</b><br /><i>Kaiming Shen and Zhi-Quan Luo</i></p> <p>12.1 Introduction 251</p> <p>12.2 System Model 252</p> <p>12.3 Random-Max Sampling (RMS) 254</p> <p>12.4 Conditional Sample Mean (CSM) 255</p> <p>12.5 Some Comments on CSM 257</p> <p>12.6 Field Tests 262</p> <p>12.7 Conclusion 268</p> <p><b>13 RIS in Wireless Information and Power Transfer 271</b><br /><i>Yang Zhao and Bruno Clerckx</i></p> <p>13.1 Introduction 271</p> <p>13.2 RIS-Aided WPT 274</p> <p>13.3 RIS-Aided WIPT 285</p> <p>13.4 Conclusion 291</p> <p><b>14 Beamforming Design for Self-Sustainable IRS-Assisted MISO Downlink Systems 297</b><br /><i>Shaokang Hu and Derrick Wing Kwan Ng</i></p> <p>14.1 Introduction 297</p> <p>14.2 System Model 299</p> <p>14.3 Problem Formulation 303</p> <p>14.4 Solution 303</p> <p>14.5 Numerical Results 307</p> <p>14.6 Summary 311</p> <p>14.7 Further Extension 311</p> <p><b>15 Optical Intelligent Reflecting Surfaces 315</b><br /><i>Hedieh Ajam and Robert Schober</i></p> <p>15.1 Introduction 315</p> <p>15.2 System and Channel Model 317</p> <p>15.3 Communication Theoretical Modeling of Optical IRSs 319</p> <p>15.4 Design of Optical IRSs for FSO Systems 327</p> <p>15.5 Simulation Results 331</p> <p>15.6 Future Extension 333</p> <p>Bibliography 334</p> <p>Index 335</p>

<p><b>Qingqing Wu, PhD,</b> is an Associate Professor with the Department of Electronic Engineering, Shanghai Jiao Tong University, China.</p> <p><b>Trung Q. Duong, PhD,</b> is a Full Professor at Memorial University of Newfoundland, Canada and a Chair Professor in Telecommunications at Queen's University Belfast, UK.</p> <p><b>Derrick Wing Kwan Ng, PhD,</b> is an Associate Professor at the University of New South Wales, Sydney, Australia.</p> <p><b>Robert Schober, PhD, </b>is a Full Professor at the Institute for Digital Communications, Friedrich-Alexander-Universität, Erlangen-Nürnberg, Germany.</p> <p><b>Rui Zhang, PhD,</b> is a Provost's Chair Professor in the Department of Electrical and Computer Engineering, National University of Singapore, Singapore.</p>

<p><b>Integrate intelligent surfaces into the wireless networks of the future.</b> <p>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. <p><i>Intelligent Surfaces Empowered 6G Wireless Network </i>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. <p><i>Intelligent Surfaces Empowered 6G Wireless Network </i>readers will also find: <ul><li> An editorial team including the original pioneers of intelligent surface technology.</li> <li> Detailed coverage of subjects including MIMO, terahertz, NOMA, energy harvesting, physical layer security, computing, sensing, machine learning, and more.</li> <li> Discussion of hardware design, signal processing techniques, and other critical aspects of IRS engineering.</li></ul> <p><i>Intelligent Surfaces Empowered 6G Wireless Network </i>is a must for students, researchers, and working engineers looking to understand this vital aspect of the coming 6G revolution.

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