Intelligent Surfaces Empowered 6G Wireless Network
(Sprache: Englisch)
INTELLIGENT SURFACES EMPOWERED 6G WIRELESS NETWORK
Integrate intelligent surfaces into the wireless networks of the future.
The next generation of wireless technology (6G) promises to transform wireless communication and human...
Integrate intelligent surfaces into the wireless networks of the future.
The next generation of wireless technology (6G) promises to transform wireless communication and human...
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INTELLIGENT SURFACES EMPOWERED 6G WIRELESS NETWORKIntegrate intelligent surfaces into the wireless networks of the future.
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.
Intelligent Surfaces Empowered 6G Wireless Network 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.
Intelligent Surfaces Empowered 6G Wireless Network readers will also find:
* An editorial team including the original pioneers of intelligent surface technology.
* Detailed coverage of subjects including MIMO, terahertz, NOMA, energy harvesting, physical layer security, computing, sensing, machine learning, and more.
* Discussion of hardware design, signal processing techniques, and other critical aspects of IRS engineering.
Intelligent Surfaces Empowered 6G Wireless Network is a must for students, researchers, and working engineers looking to understand this vital aspect of the coming 6G revolution.
Inhaltsverzeichnis zu „Intelligent Surfaces Empowered 6G Wireless Network “
About the Editors xiiiList of Contributors xv
Preface xxi
Acknowledgement xxiii
Part I Fundamentals of IRS 1
1 Introduction to Intelligent Surfaces 3
Kaitao Meng, Qingqing Wu, Trung Q. Duong, Derrick Wing Kwan Ng, Robert Schober, and Rui Zhang
1.1 Background 3
1.2 Concept of Intelligent Surfaces 5
1.3 Advantages of Intelligence Surface 7
1.4 Potential Applications 8
1.5 Conclusion 12
2 IRS Architecture and Hardware Design 15
Zijian Zhang, Yuhao Chen, Qiumo Yu, and Linglong Dai
2.1 Metamaterials: Basics of IRS 15
2.2 Programmable Metasurfaces 16
2.3 IRS Hardware Design 18
2.4 State-of-the-Art IRS Prototype 23
3 On Path Loss and Channel Reciprocity of RIS-Assisted Wireless Communications 37
Wankai Tang, Jinghe Wang, Jun Yan Dai, Marco Di Renzo, Shi Jin, Qiang Cheng, and Tie Jun Cui
3.1 Introduction 37
3.2 Path Loss Modeling and Channel Reciprocity Analysis 39
3.3 Path Loss Measurement and Channel Reciprocity Validation 47
3.4 Conclusion 54
4 Intelligent Surface Communication Design: Main Challenges and Solutions 59
Kaitao Meng, Qingqing Wu, and Rui Zhang
4.1 Introduction 59
4.2 Channel Estimation 59
4.3 Passive Beamforming Optimization 65
4.4 IRS Deployment 73
4.5 Conclusion 79
Part II IRS for 6G Wireless Systems 83
5 Overview of IRS for 6G and Industry Advance 85
Ruiqi (Richie) Liu, Konstantinos D. Katsanos, Qingqing Wu, and George C. Alexandropoulos
5.1 IRS for 6G 85
5.2 Industrial Progresses 98
6 RIS-Aided Massive MIMO Antennas 117
Stefano Buzzi, Carmen D'Andrea, and Giovanni Interdonato
6.1 Introduction 117
6.2 System Model 119
6.3 Uplink/Downlink Signal Processing 123
6.4 Performance Measures 126
6.5 Optimization of the RIS Phase
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Shifts 128
6.6 Numerical Results 130
6.7 Conclusions 134
7 Localization, Sensing, and Their Integration with RISs 139
George C. Alexandropoulos, Hyowon Kim, Jiguang He, and Henk Wymeersch
7.1 Introduction 139
7.2 RIS Types and Channel Modeling 142
7.3 Localization with RISs 147
7.4 Sensing with RISs 154
7.5 Conclusion and Open Challenges 159
8 IRS-Aided THz Communications 167
Boyu Ning and Zhi Chen
8.1 IRS-Aided THz MIMO System Model 167
8.2 Beam Training Protocol 168
8.3 IRS Prototyping 175
8.4 IRS-THz Communication Applications 182
9 Joint Design of Beamforming, Phase Shifting, and Power Allocation in a Multi-cluster IRS-NOMA Network 187
Ximing Xie, Fang Fang, and Zhiguo Ding
9.1 Introduction 187
9.2 System Model and Problem Formulation 190
9.3 Alternating Algorithm 193
9.4 Simulation Result 200
9.5 Conclusion 203
10 IRS-Aided Mobile Edge Computing: From Optimization to Learning 207
Xiaoyan Hu, Kai-Kit Wong, Christos Masouros, and Shi Jin
10.1 Introduction 207
10.2 System Model and Objective 208
10.3 Optimization-Based Approaches to IRS-Aided MEC 211
10.4 Deep Learning Approaches to IRS-Aided MEC 216
10.5 Comparative Evaluation Results 222
10.6 Conclusions 226
11 Interference Nulling Using Reconfigurable Intelligent Surface 229
Tao Jiang, Foad Sohrabi, and Wei Yu
11.1 Introduction 229
11.2 System Model 231
11.3 Interference Nulling via RIS 232
11.4 Learning to Minimize Interference 241
6.6 Numerical Results 130
6.7 Conclusions 134
7 Localization, Sensing, and Their Integration with RISs 139
George C. Alexandropoulos, Hyowon Kim, Jiguang He, and Henk Wymeersch
7.1 Introduction 139
7.2 RIS Types and Channel Modeling 142
7.3 Localization with RISs 147
7.4 Sensing with RISs 154
7.5 Conclusion and Open Challenges 159
8 IRS-Aided THz Communications 167
Boyu Ning and Zhi Chen
8.1 IRS-Aided THz MIMO System Model 167
8.2 Beam Training Protocol 168
8.3 IRS Prototyping 175
8.4 IRS-THz Communication Applications 182
9 Joint Design of Beamforming, Phase Shifting, and Power Allocation in a Multi-cluster IRS-NOMA Network 187
Ximing Xie, Fang Fang, and Zhiguo Ding
9.1 Introduction 187
9.2 System Model and Problem Formulation 190
9.3 Alternating Algorithm 193
9.4 Simulation Result 200
9.5 Conclusion 203
10 IRS-Aided Mobile Edge Computing: From Optimization to Learning 207
Xiaoyan Hu, Kai-Kit Wong, Christos Masouros, and Shi Jin
10.1 Introduction 207
10.2 System Model and Objective 208
10.3 Optimization-Based Approaches to IRS-Aided MEC 211
10.4 Deep Learning Approaches to IRS-Aided MEC 216
10.5 Comparative Evaluation Results 222
10.6 Conclusions 226
11 Interference Nulling Using Reconfigurable Intelligent Surface 229
Tao Jiang, Foad Sohrabi, and Wei Yu
11.1 Introduction 229
11.2 System Model 231
11.3 Interference Nulling via RIS 232
11.4 Learning to Minimize Interference 241
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Autoren-Porträt
Qingqing Wu, PhD, is an Associate Professor with the Department of Electronic Engineering, Shanghai Jiao Tong University, China.Trung Q. Duong, PhD, is a Full Professor at Memorial University of Newfoundland, Canada and a Chair Professor in Telecommunications at Queen's University Belfast, UK.
Derrick Wing Kwan Ng, PhD, is an Associate Professor at the University of New South Wales, Sydney, Australia.
Robert Schober, PhD, is a Full Professor at the Institute for Digital Communications, Friedrich-Alexander-Universität, Erlangen-Nürnberg, Germany.
Rui Zhang, PhD, is a Provost's Chair Professor in the Department of Electrical and Computer Engineering, National University of Singapore, Singapore.
Bibliographische Angaben
- 2023, 1. Auflage, 368 Seiten, Masse: 16,1 x 23,7 cm, Gebunden, Englisch
- Herausgegeben: Qingqing Wu, Trung Q. Duong, Derrick Wing Kwan Ng, Robert Schober, Rui Zhang
- Verlag: Wiley & Sons
- ISBN-10: 1119913098
- ISBN-13: 9781119913092
- Erscheinungsdatum: 14.12.2023
Sprache:
Englisch
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