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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- 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 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.
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
About the Editors
List of Contributors
Preface
Acknowledgement
Part I: Fundamentals of IRS
1 Introduction to Intelligent Surfaces
1.1 Background
1.2 Concept of Intelligent Surfaces
1.3 Advantages of Intelligence Surface
1.4 Potential Applications
1.5 Conclusion
Bibliography
Note
2 IRS Architecture and Hardware Design
2.1 Metamaterials: Basics of IRS
2.2 Programmable Metasurfaces
2.3 IRS Hardware Design
2.4 State-of-the-Art IRS Prototype
Bibliography
3 On Path Loss and Channel Reciprocity of RIS-Assisted Wireless Communications
3.1 Introduction
3.2 Path Loss Modeling and Channel Reciprocity Analysis
3.3 Path Loss Measurement and Channel Reciprocity Validation
3.4 Conclusion
3.A Appendix
Bibliography
4 Intelligent Surface Communication Design: Main Challenges and Solutions
4.1 Introduction
4.2 Channel Estimation
4.3 Passive Beamforming Optimization
4.4 IRS Deployment
4.5 Conclusion
Bibliography
Part II: IRS for 6G Wireless Systems
5 Overview of IRS for 6G and Industry Advance
5.1 IRS for 6G
5.2 Industrial Progresses
Bibliography
6 RIS-Aided Massive MIMO Antennas
6.1 Introduction
6.2 System Model
6.3 Uplink/Downlink Signal Processing
6.4 Performance Measures
6.5 Optimization of the RIS Phase Shifts
6.6 Numerical Results
6.7 Conclusions
6.A Appendix
Bibliography
Notes
7 Localization, Sensing, and Their Integration with RISs
7.1 Introduction
7.2 RIS Types and Channel Modeling
7.3 Localization with RISs
7.4 Sensing with RISs
7.5 Conclusion and Open Challenges
Bibliography
Note
8 IRS-Aided THz Communications
8.1 IRS-Aided THz MIMO System Model
8.2 Beam Training Protocol
8.3 IRS Prototyping
8.4 IRS-THz Communication Applications
Bibliography
9 Joint Design of Beamforming, Phase Shifting, and Power Allocation in a Multi-cluster IRS-NOMA Network
9.1 Introduction
9.2 System Model and Problem Formulation
9.3 Alternating Algorithm
9.4 Simulation Result
9.5 Conclusion
Bibliography
10 IRS-Aided Mobile Edge Computing: From Optimization to Learning
10.1 Introduction
10.2 System Model and Objective
10.3 Optimization-Based Approaches to IRS-Aided MEC
10.4 Deep Learning Approaches to IRS-Aided MEC
10.5 Comparative Evaluation Results
10.6 Conclusions
Bibliography
Note
11 Interference Nulling Using Reconfigurable Intelligent Surface
11.1 Introduction
11.2 System Model
11.3 Interference Nulling via RIS
11.4 Learning to Minimize Interference
11.5 Conclusions
Bibliography
12 Blind Beamforming for IRS Without Channel Estimation
12.1 Introduction
12.2 System Model
12.3 Random-Max Sampling (RMS)
12.4 Conditional Sample Mean (CSM)
12.5 Some Comments on CSM
12.6 Field Tests
12.7 Conclusion
Bibliography
13 RIS in Wireless Information and Power Transfer
13.1 Introduction
13.2 RIS-Aided WPT
13.3 RIS-Aided WIPT
13.4 Conclusion
Bibliography
Notes
14 Beamforming Design for Self-Sustainable IRS-Assisted MISO Downlink Systems
14.1 Introduction
14.2 System Model
14.3 Problem Formulation
14.4 Solution
14.5 Numerical Results
14.6 Summary
14.7 Further Extension
Bibliography
Notes
15 Optical Intelligent Reflecting Surfaces
15.1 Introduction
15.2 System and Channel Model
15.3 Communication Theoretical Modeling of Optical IRSs
15.4 Design of Optical IRSs for FSO Systems
15.5 Simulation Results
15.6 Future Extension
Bibliography
Index
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Symbols and definitions.
Table 3.2 Parameters of the RISs and antennas employed in the measurement....
Table 3.3 Experimental results for validating RIS channel reciprocity.
Chapter 5
Table 5.1 Summary of research projects dedicated to RIS.
Table 5.2 Summary of white papers dedicated to RIS (google scholar, search:...
Table 5.3 Summary of completed work items in ETSI ISG RIS.
Chapter 7
Table 7.1 Taxonomy of the available RIS types in terms of operational featu...
Table 7.2 Requirements for 3D localization with respect to the type of meas...
Table 7.3 Localization scenarios with reflective RISs.
Chapter 10
Table 10.1 Parameters related to training and testing of the DNNs.
Table 10.2 Processing time of the overviewed algorithms.
Table 10.3 Simulation parameters.
Chapter 12
Table 12.1 Performance of the different algorithms.
Chapter 14
Table 14.1 Simulation parameters.
Chapter 15
Table 15.1 System parameters.
List of Illustrations
Chapter 1
Figure 1.1 Architecture of intelligent surfaces.
Figure 1.2 Smart city empowered by intelligent surfaces.
Figure 1.3 Illustration of intelligent surface applications.
Chapter 2
Figure 2.1 Programmable metasurfaces made of different stimuli-sensitive met...
Figure 2.2 Four typical types of feeding system.
Figure 2.3 1-bit IRS element. (a) Perspective view. (b) Side view. (c) Simul...
Figure 2.4 An example of IRS array with 2304 elements.
Figure 2.5 An example of IRS control system.
Figure 2.6 (a) Waveguide test, (b) Near-field test, (c) Far-field test.
Figure 2.7 PIN diode-based IRS.
Figure 2.8 Two IRS prototypes (256 elements).
Figure 2.9 The IRS prototypes (2304 elements).
Figure 2.10 A comparison between passive IRSs and active IRSs.
Figure 2.11 A 64-element active IRS.
Figure 2.12 Active IRS prototype and experimental measurement results.
Figure 2.13 Designing diagram of the transmitter.
Figure 2.14 Frame structure.
Figure 2.15 Designing diagram of the receiver.
Figure 2.16 Designing diagram of the system.
Figure 2.17 The IRS prototype developed by MIT.
Figure 2.18 The element structure of the IRS developed by MIT.
Figure 2.19 The design principle of the IRS radiation coefficient proposed b...
Figure 2.20 The scenario where IRS can extend the coverage of signals.
Figure 2.21 The test scenario of China Mobile: fixed points.
Figure 2.22 The prototype.
Figure 2.23 The test scenario.
Chapter 3
Figure 3.1 A typical RIS-assisted wireless communication system.
Figure 3.2 Two typical application scenarios of RIS-assisted wireless commun...
Figure 3.3 Photographs of the two employed RISs. (a) RIS 1. (b) RIS 2.
Figure 3.4 Photographs of the measurement systems. (a) Path loss measurement...
Figure 3.5 Validation of the RIS path loss by using RIS 1. (a) Path loss ver...
Figure 3.6 Validation of the RIS path loss by using RIS 2. (a) Path loss ver...
Chapter 4
Figure 4.1 Two practical intelligent surface configurations and their respec...
Figure 4.2 IRS-aided multiuser communication system.
Figure 4.3 IRS deployment in a point-to-point communication system. (a) Sing...
Figure 4.4 IRS deployment in a multiuser communication network. (a) Distribu...
Figure 4.5 Illustration of a hybrid wireless network with active BSs and pas...
Chapter 5
Figure 5.1 Connectivity and reliability boosted by a single RIS.
Figure 5.2 RIS-empowered downlink communication of two BS-UE pairs, where ea...
Figure 5.3 RIS-aided systems where connectivity is enabled by multiple RISs....
Figure 5.4 A multi-tenancy scenario with two BS-UE pairs and a shared RIS th...
Figure 5.5 RIS-enabled localization and sensing.
Figure 5.6 Multiple RIS-enabled localization and sensing in the vicinity of ...
Figure 5.7 RIS-empowered sustainability and secrecy where the BS-to-intended...
Figure 5.8 RIS-aided sustainability and secrecy where the BS-to-intended lin...
Figure 5.9 RIS-aided sustainability and secrecy where the BS-to-intended lin...
Chapter 6
Figure 6.1 (a) A possible configuration of the considered RIS-aided antenna ...
Figure 6.2 Eigenvalues of as a function of , for the case , and .
Figure 6.3 Normalized mean squares error (NMSE) versus the number of RIS ele...
Figure 6.4 CDF of the Spectral efficiency (SE) per user in the case of omnid...
Figure 6.5 CDF of the Spectral efficiency (SE) per user in the case of omnid...
Figure 6.6 CDF of the Spectral efficiency (SE) per user in the case of direc...
Figure 6.7 CDF of the Spectral efficiency (SE) per user in the case of direc...
Chapter 7
Figure 7.1 The individual channel matrices in RIS-assisted wireless links.
Figure 7.2 Geometric-based localization techniques via (a)
ToA
and (b) AoD m...
Figure 7.3 Overview of selected localization scenarios with reflective RISs....
Figure 7.4 STAR-RIS-enabled simultaneous indoor and outdoor 3D localization ...
Figure 7.5 CRB on the estimation of the 3D Cartesian coordinates of the two ...
Figure 7.6 3D user localization system with multiple spatially distributed r...
Figure 7.7 The RMSE of the localization error in meters versus the source tr...
Figure 7.8 3D user localization with a partially-connected receiving RIS com...
Figure 7.9 Localization performance of the ANM-based technique versus the tr...
Figure 7.10 A signal path enabled by an RIS, which is deployed to overcome a...
Figure 7.11 Link budgets in dB of the single- and double-bounce signals in R...
Figure 7.12 An RIS-enabled SLAM scenario with one-bounce signal propagation ...
Figure 7.13 SLAM performance with respect to the time index for different ...
Chapter 8
Figure 8.1 A point-to-point IRS-assisted UM-MIMO system.
Figure 8.2 Path angles in IRS-assisted UM-MIMO systems.
Figure 8.3 Beam modes for active terminal and passive terminal.
Figure 8.4 Primary idea of the IRS-assisted joint beam training.
Figure 8.5 Practical beam training procedure for IRS-assisted systems.
Figure 8.6 The performance of different schemes in THz LoS-blockage case.
Figure 8.7 The performance of different schemes in THz non-blockage case.
Figure 8.8 Active beam steering devices.(a) phased array by phase modula...
Figure 8.9 Representative THz IRSs: (A) HEMT-based ridged waveguide array; (...
Figure 8.10 The methodology of logically fractional coding: (a) the angular ...
Figure 8.11 Schematic diagrams of the 2DEG-regulated IRS: (a) 2DEG-embedded ...
Figure 8.12 Promising applications of THz-IRS communications in six scenario...
Chapter 9
Figure 9.1 An IRS NOMA system model.
Figure 9.2 The transmit power versus the number of reflecting elements at th...
Figure 9.3 The transmit power versus the number of reflecting elements at th...
Figure 9.4 The transmit power versus the minimum date rate of the central us...
Figure 9.5 The transmit power versus the distance between the IRS and the BS...
Chapter 10
Figure 10.1 An illustration of the typical IRS-aided MEC architecture.
Figure 10.2 Two typical IRS-aided edge computation offloading scenarios....
Figure 10.3 The architecture for obtaining the solutions of with the CSI-b...
Figure 10.4 The architecture for obtaining the solutions of with the locat...
Figure 10.5 The average running time versus the number of UEs (): (a) optim...
Figure 10.6 The TCTB of UEs versus the uniform energy budget , .
Figure 10.7 The TCTB of UEs versus the number of UEs with .
Figure 10.8 The TCTB of UEs versus the uniform energy budget , .
Chapter 11
Figure 11.1 System model of an RIS-assisted -user interference environment....
Figure 11.2 Empirical interference nulling probability versus number of RIS ...
Figure 11.3 Number of RIS elements versus number of transceiver pairs in t...
Figure 11.4 Empirical interference nulling probability versus number of RIS ...
Figure 11.5 Empirical interference nulling probability versus direct-to-refl...
Figure 11.6 Conventional method runs the alternating projection algorithm fr...
Figure 11.7 Proposed approach uses a deep neural network to learn a good ini...
Figure 11.8 Performance comparison for a system with RIS elements and tr...
Figure 11.9 Performance comparison for a system with RIS elements, trans...
Chapter 12
Figure 12.1 Consider four sectors ; each sector spans an angle of . For ,...
Figure 12.2 Consider four sectors ; each sector spans an angle of . For ,...
Figure 12.3 A panoramic view of the field test site. The base station is loc...
Figure 12.4 The ON-OFF state of a PIN diode results in two distinct resonanc...
Figure 12.5 The IRS is formed by a array of reflecting tiles. Each reflect...
Figure 12.6 A satellite image of the field test site. The base station and t...
Figure 12.7 The view from the user terminal toward the IRS.
Figure 12.8 SINR boost for SISO transmission.
Figure 12.9 RSRP boost for SISO transmission.
Figure 12.10 SE increment for MIMO transmission.
Chapter 13
Figure 13.1 Block diagram of a closed-loop adaptive RIS-aided WPT architectu...
Figure 13.2 Antenna equivalent circuit and a single-diode half-wave rectifie...
Figure 13.3 Power response and RF-to-DC conversion efficiency of a single-di...
Figure 13.4 Average single-user output DC current versus and at 5.18 GHz...
Figure 13.5 Combining techniques for multiantenna ERs. (a) RF combing and (b...
Figure 13.6 Direct CSI acquisition schemes at the ET. (a) Forward-link train...
Figure 13.7 5.8 GHz RIS-aided MIMO WPT prototype testbed. (a) Phased array t...
Figure 13.8 Measured receive power regions for different multifocus techniqu...
Figure 13.9 Information and energy flows in different WIPT schemes. Dashed a...
Figure 13.10 Single-antenna SWIPT receiver architectures. (a) Ideal receiver...
Figure 13.11 Sorted equivalent subchannel amplitude versus with and withou...
Figure 13.12 Average RE region versus with a 20-element RIS at 2.4 GHz wit...
Chapter 14
Figure 14.1 A self-sustainable IRS-assisted wireless communication system.
Figure 14.2 An energy harvesting block diagram of the self-sustainable IRS....
Figure 14.3 The flow chart of the proposed iterative algorithm.
Figure 14.4 Simulation setup.
Figure 14.5 (a) Average system sum-rate (bits/s/Hz) versus the horizontal di...
Figure 14.6 The impact of the total number of IRS elements on the average sy...
Chapter 15
Figure 15.1 An intelligent reflecting surface (IRS)-assisted free space opti...
Figure 15.2 Illustration of the proposed equivalent mirror-assisted system: ...
Figure 15.3 IRS-sharing protocols. The IRS is divided into tiles for the T...
Figure 15.4 The GML factor versus the IRS length.
Figure 15.5 The outage probability versus the receiver lens angle.
Guide
Cover
Title Page
Copyright
About the Editors
List of Contributors
Preface
Acknowledgement
Table of Contents
Begin Reading
Index
End User License Agreement
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IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial BoardSarah Spurgeon, Editor in Chief
Jón Atli Benediktsson Anjan Bose James Duncan Amin Moeness Desineni Subbaram Naidu
Behzad Razavi Jim Lyke Hai Li Brian Johnson
Jeffrey Reed Diomidis Spinellis Adam Drobot Tom Robertazzi Ahmet Murat Tekalp
Intelligent Surfaces Empowered 6G Wireless Network
Edited by
Qingqing Wu
Shanghai Jiao Tong UniversityChina
Trung Q. Duong
Memorial University of Newfoundland Canada
Queen’s University Belfast United Kingdom
Derrick Wing Kwan Ng
The University of New South Wales Australia
Robert Schober
University of Erlangen-Nuremberg Germany
Rui Zhang
National University of Singapore Singapore
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Library of Congress Cataloging-in-Publication Data:
Names: Wu, Qingqing (Professor), author.
Title: Intelligent surfaces empowered 6G wireless network / Qingqing Wu [and four others].
Description: Hoboken, New Jersey : Wiley, [2024] | Includes index.
Identifiers: LCCN 2023047400 (print) | LCCN 2023047401 (ebook) | ISBN 9781119913092 (hardback) | ISBN 9781119913108 (adobe pdf) | ISBN 9781119913115 (epub)
Subjects: LCSH: Surfaces (Technology) | Smart materials. | 6G mobile communication systems.
Classification: LCC TA418.7 .W9 2024 (print) | LCC TA418.7 (ebook) | DDC 621.3845/6–dc23/eng/20231107
LC record available at https://lccn.loc.gov/2023047400
LC ebook record available at https://lccn.loc.gov/2023047401
Cover Design: WileyCover Image: © zf L/Getty Images
About the Editors
Qingqing Wu is an Associate Professor at Shanghai Jiao Tong University. His current research interest includes intelligent reflecting surface (IRS), UAV communications, and reconfigurable MIMO. He is listed as the Clarivate ESI Highly Cited Researcher from 2021 to 2023. He was the recipient of the IEEE Communications Society Asia Pacific Best Young Researcher Award in 2022.
Trung Q. Duong is a Full Professor at Memorial University of Newfoundland, Canada and also a Chair Professor in Telecommunications at Queen's University Belfast, United Kingdom. He has been awarded the Royal Academy of Engineering Research Chair (2021–2025) and Royal Academy of Engineering Research Fellowship (2016–2020). He received the Newton Prize 2017 from the UK government. He is a Fellow of IEEE.
Derrick Wing Kwan Ng is currently a Scientia Associate Professor at the University of New South Wales, Sydney, Australia and an IEEE Fellow. He is the Editor of IEEE Transactions on Communications and the Associate Editor-in-Chief of IEEE Open Journal of the Communications Society. His research interests include global optimization, physical layer security, IRS-assisted communication, UAV-assisted communication, wireless information and power transfer, and green (energy-efficient) wireless communications.
Robert Schober is an Alexander von Humboldt Professor and the Chair for Digital Communication at FAU. His research interests fall into the broad areas of communication theory, wireless and molecular communications, and statistical signal processing. Currently, he serves as Senior Editor of the Proceedings of the IEEE and as ComSoc President-Elect.
Dr. Rui Zhang (Fellow of IEEE, Fellow of the Academy of Engineering Singapore) received the PhD degree from Stanford University in Electrical Engineering in 2007. He is now a Principal's Diligence Chair Professor in School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen. His current research interests include wireless information and power transfer, UAV/satellite communications, IRS, and reconfigurable MIMO.
List of Contributors
Hedieh AjamDepartment of Electrical, Electronics, and Communication EngineeringInstitute for Digital CommunicationsFriedrich-Alexander-University Erlangen NürnbergErlangenGermany
George C. AlexandropoulosDepartment of Informatics and TelecommunicationsNational and Kapodistrian University of Athens,AthensGreece
and
Technology Innovation InstituteMasdar CityAbu DhabiUnited Arab Emirates
Stefano BuzziDepartment of Electric and Information Engineering (DIEI)University of Cassino and Southern Latium,Cassino (FR)Italy
and
Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT)ParmaItaly
and
Dipartimento di ElettronicaInformazione e BioingegneriaPolitecnico di MilanoMilanoItaly
Zhi ChenNational Key Laboratory of Wireless CommunicationsUniversity of Electronic Science and Technology of ChinaChengduSichuanChina
Yuhao ChenDepartment of Electronic EngineeringTsinghua UniversityBeijingChina
Qiang ChengState Key Laboratory of Millimeter WavesSoutheast UniversityNanjingChina
Bruno ClerckxDepartment of Electrical and Electronics EngineeringImperial College LondonLondonUK
Tie Jun CuiState Key Laboratory of Millimeter WavesSoutheast UniversityNanjingChina
Jun Yan DaiState Key Laboratory of Millimeter WavesSoutheast UniversityNanjingChina
Linglong DaiDepartment of Electronic EngineeringTsinghua UniversityBeijingChina
Carmen D'AndreaDepartment of Electric and Information Engineering (DIEI)University of Cassino and Southern LatiumCassino (FR)Italy
and
Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT)ParmaItaly
Zhiguo DingSchool of Electrical and Electronic EngineeringThe University of ManchesterManchesterUK
Marco Di RenzoUniversité Paris-SaclayCNRS, CentraleSupélec, Laboratoire des Signaux et SystèmesGif-sur-YvetteFrance
Trung Q. DuongMemorial University of NewfoundlandCanada
and
Queen's University BelfastUK
Fang FangDepartment of Electrical and Computer Engineering and the Department of Computer ScienceWestern UniversityLondonCanada
Jiguang HeTechnology Innovation InstituteAbu DhabiUnited Arab Emirates
Shaokang HuSchool of Electrical Engineering and TelecommunicationsUniversity of New South WalesSydneyNSWAustralia
Xiaoyan HuSchool of Information and Communications EngineeringXi'an Jiaotong UniversityXi'anChina
Giovanni InterdonatoDepartment of Electric and Information Engineering (DIEI)University of Cassino and Southern LatiumCassino (FR)Italy
and
Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT)ParmaItaly
Shi JinNational Mobile Communications Research LaboratorySoutheast UniversityNanjingChina
and
School of Information Science and EngineeringSoutheast UniversityNanjingChina
Tao JiangThe Edward S. Rogers Sr. Department of Electrical and Computer EngineeringUniversity of TorontoTorontoOntarioCanada
Konstantinos D. KatsanosDepartment of Informatics and TelecommunicationsNational and Kapodistrian University of AthensAthensGreece
Hyowon KimDepartment of Electronics EngineeringChungnam National UniversityDaejeonSouth Korea
Ruiqi (Richie) LiuState Key Laboratory of Mobile Network and Mobile Multimedia TechnologyShenzhenChina
and
Wireless and Computing Research InstituteZTE CorporationBeijingChina
Zhi-Quan LuoShenzhen Research Institute of Big DataShenzhenChina
Christos MasourosDepartment of Electronic and Electrical EngineeringUniversity College LondonLondonUK
Kaitao MengState Key Laboratory of Internet of Things for Smart CityUniversity of MacauMacauChina
Derrick Wing Kwan NgSchool of Electrical Engineering and TelecommunicationsUniversity of New South WalesSydneyNSWAustralia
Boyu NingNational Key Laboratory of Wireless CommunicationsUniversity of Electronic Science and Technology of ChinaChengduSichuanChina
Robert SchoberDepartment of Electrical, Electronics and Communication EngineeringInstitute for Digital CommunicationsFriedrich-Alexander-University Erlangen NürnbergErlangenGermany
Kaiming ShenSchool of Science and EngineeringThe Chinese University of Hong Kong (Shenzhen)ShenzhenChina
Foad SohrabiNokia Bell LabsMurray HillNJUSA
Wankai TangNational Mobile Communications Research LaboratorySoutheast UniversityNanjingChina
Jinghe WangNational Mobile Communications Research LaboratorySoutheast UniversityNanjingChina
Kai-Kit WongDepartment of Electronic and Electrical EngineeringUniversity College LondonLondonUK
Qingqing WuDepartment of Electronic EngineeringShanghai Jiaotong UniversityShanghaiChina
Henk WymeerschDepartment of Electrical EngineeringChalmers University of TechnologyGothenburgSweden
Ximing XieSchool of Electrical and Electronic EngineeringThe University of ManchesterManchesterUK
Qiumo YuDepartment of Electronic EngineeringTsinghua UniversityBeijingChina
Wei YuThe Edward S. Rogers Sr. Department of Electrical and Computer EngineeringUniversity of TorontoTorontoOntarioCanada
Rui ZhangDepartment of Electrical and Computer EngineeringNational University of SingaporeSingapore
Zijian ZhangDepartment of Electronic EngineeringTsinghua UniversityBeijingChina
Yang ZhaoDepartment of Electrical and Electronics EngineeringImperial College LondonLondonUK
Preface
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 come to be recognized as a critical component for enabling the future 6G. The next phase of wireless technology demands engineers and researchers familiar with this technology and 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.
10 October 1991
Qingqing Wu
29 October 1979
Trung Q. Duong
5 November 1984
Derrick Wing Kwan Ng
10 June 1971
Robert Schober
14 September 1976
Rui Zhang
Acknowledgement
Q. Wu's work is supported by National Key R&D Program of China (2023YFB2905000), NSFC 62371289, NSFC 62331022, FDCT under Grant 0119/2020/A3.
Part IFundamentals of IRS
1Introduction to Intelligent Surfaces
Kaitao Meng1, Qingqing Wu2, Trung Q. Duong3,4, Derrick Wing Kwan Ng5, Robert Schober6, and Rui Zhang7
1State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China
2Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai, Country
3Queen's University Belfast, United Kingdom
4Memorial University of Newfoundland, Canada
5School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia
6Institute for Digital Communications, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany
7Department of Electrical and Computer Engineering, National University of Singapore, Singapore
1.1 Background
In the forthcoming era of Internet of Everything (IoE), worldwide mobile data traffic is expected to grow at an annual rate of roughly 55% between 2020 and 2030, eventually reaching 5016 exabytes by 2030 (Andrews et al. 2014). According to a recent report, by 2025, the number of connected devices will increase to more than 30 billion globally. Also, considering the rapid emergence of new wireless applications such as smart cities, intelligent transportation, and augmented/virtual reality (Nikitas et al. 2020), it is foreseen that the fifth generation (5G) may encounter capacity and performance limitations in supporting the accommodating these low-latency, high-capacity, ultra-reliable, and massive-connectivity wireless communication services. In addition to supporting these high-quality wireless communications, next-generation wireless networks are required to provide several other heterogeneous services, e.g., extremely high-accuracy sensing (Meng et al. 2023) and low-latency computing capabilities. Specifically, the representative key performance indicators advocated by the sixth generation (6G) are summarized as follows (Letaief et al. 2019; Tataria et al. 2021; Jiang et al. 2021):
The
peak data rates
under ideal wireless propagation conditions are higher than terabits per second for both indoor and outdoor connections, which is 100–1000 times that of the state-of-the-art 5G;
The
energy efficiency
of 6G is 10–100 times that of 5G to achieve green communications;
Five times more the
spectral efficiency
of 5G is pursued by utilizing the limited frequency spectrum more efficiently;
Connection density
could be 10 times that of 5G, about to satisfy the high demand for massive connectivity in IoE and enhanced mMTC;
Reliability
is larger than % to support more enhanced ultra-reliable and low-latency communication (URLLC) compared to 5G;
Shorter than
latency
is required to support numerous enhanced URLLC;
Centimeter (cm)-level
positioning accuracy
in three-dimensional (3D) space is required to fulfill the harsh demands of various vertical and industrial applications, instead of requiring meter (m)-level positioning accuracy in two-dimensional space (2D) space as in 5G;
However, even with the existing technologies such as massive multiple-input-multiple-output (M-MIMO) and millimeter wave (mmWave)/terahertz (THz), the abovementioned performance requirements for IoE services may not be fully realized due to the following reasons:
First, dense deployment of active nodes such as access points (APs), base stations (BSs), and relays can shorten the communication distance, thereby enhancing network coverage and capacity, which, however, incurs higher energy consumption and backhaul/deployment/maintenance costs.
Second, installing substantially more antennas at APs/BSs/relays to take advantage of the huge M-MIMO gains inevitably results in increased hardware/energy costs and signal processing complexity, as well as exacerbates more severe and complicated network interference issues (Lu et al.
2014
).
Third, migrating to higher frequency bands, such as mmWave and THz frequencies, is able to harness their larger available unlicensed bandwidth (Niu et al.
2015
). Yet, it requires the deployment of more active nodes and more antennas to compensate for the associated severer propagation attenuation over distance.
Fourth, the diffraction and scattering effects of high-frequency radio are weakened, such that propagating electromagnetic waves can be easily blocked by obstacles such as urban buildings. As a result, the effective coverage radius of APs/BSs decreases while the potential number of blind spots increase. Thus, it will be difficult to ensure universal coverage and wireless services exploiting traditional cellular technologies.
Taking into account the above limitations and issues, it is highly imperative to develop disruptively new and innovative technologies to realize spectrum- and energy-efficient and cost-effective capacity growth of future wireless networks.
1.2 Concept of Intelligent Surfaces
The fundamental challenge to achieve high-throughput and ultra-reliable wireless communication arises from time-varying wireless channels caused by user mobility (Neely 2006). The conventional approaches to this challenge are mainly to utilize various modulation, coding, and diversity techniques to counteract for channel fading, or to adapt to the channel through adaptive power/rate control and beamforming techniques (Chen and Laneman 2006). Traditionally, the fading wireless channel is treated as uncontrollable block-box and becomes one of the main limiting factors for performance improvement.
Motivated by the above, the advancing radio environment reconfiguration technique has recently emerged as a promising new paradigm to achieve smart and highly controllable ratio propagation channels for next-generation wireless communication systems. This has been achieved by the recently proposed intelligent surfaces, such as intelligent reflecting/refracting surfaces (IRSs) (Wu and Zhang 2019; Zheng et al. 2022; Huang et al. 2022), also called reconfigurable intelligent surfaces (RISs) (ElMossallamy et al. 2020; Liu et al. 2021), or large intelligent surfaces (LISs) (Hu et al. 2018; Jung et al. 2021). Generally speaking, an intelligent surface is a planar surface comprising a large number of passive reflecting/refracting elements, each of which is able to induce a controllable amplitude and/or phase change to the incident signal independently. More specifically, intelligent surfaces can be realized by adopting metamaterial or patch-array-based technologies (Liu et al. 2021). With a dense deployment of smart surfaces in a wireless network and smartly coordinating their reflection/refraction, the signal propagation/radio between transmitters and receivers can be flexibly reconfigured to achieve the desired realizations and/or distributions. This paradigm serves as a new approach to fundamentally address the wireless channel fading impairment and interference issues, and it is possible to achieve a dramatic improvement in wireless communication capacity and reliability.
As shown in Fig. 1.1, a typical architecture of an intelligent surface consists of three layers and a smart controller. On the right-most layer, a large number of metal patches are printed on a dielectric substrate to directly interact with the incident signal. Behind this layer, a copper plate is adopted to avoid signal energy leakage. Finally, the left-most layer is the control board responsible for adjusting the reflection/refractionamplitude/phase shift of each element, triggered by a smart controller connected to the intelligent surface (Jian et al. 2022). Practically, a field-programmable gate array (FPGA) can be implemented as a controller, which also acts as a gateway, communicating and coordinating with other network components (such as BS, AP, and user terminals) through an out-of-band wireless link to achieve low power consumption rate information is exchanged with them.
Figure 1.1 Architecture of intelligent surfaces.
Source: Adapted from (Wu et al. 2021).
In the following, we further highlight the main differences between intelligent surfaces and other related technologies such as active relaying and backscatter communication. First, compared to active wireless relays that assist source–destination communication through signal regeneration and retransmission, intelligent surfaces neither demodulate nor generate any information sources but act as passive array (or weakly active array, such as active intelligent surfaces; Long et al. 2021) to reflect/refract the received signals. Additionally, active relays generally operates with a half-duplex (HD) protocol and are therefore generally less spectral efficient than that of intelligent surfaces operating in full-duplex (FD) mode. Second, a tag reader in backscatter communication requires to perform interference cancellation at its receiver to decode the radio frequency identification tags' message (Wang et al. 2016). Actually, intelligent surfaces can also modulate its information during reflection, but which are primarily designed to facilitate existing communication links.
1.3 Advantages of Intelligence Surface
Benefiting from the appealing ability to reconfigure wireless channels, it is envisioned that intelligent surfaces are suitable to be massively deployed in wireless networks and possess various practical advantages for implementation as follows:
First, in general, intelligent surfaces mainly reflect/refract incident signals in a passive manner without requiring any transmit radio-frequency (RF) chains. Thus, they can be implemented with orders-of-magnitude lower hardware/energy costs as compared to traditional active antenna arrays or the recently proposed active surfaces (Hu et al.
2018
).
Second, intelligent surfaces are not only able to reconfigure the wireless propagation environment by compensating for the power loss over long distances but also optimize channel rank for improving the potential spatial multiplexing gain and spectral efficiency by introducing more controllable signal paths between the transmitters and receivers in multi-antenna communications.
Third, intelligent surfaces operate in full-duplex mode and are free of any antenna noise amplification as well as self-interference (Abdullah et al.
2020
), which thus offers competitive advantages over traditional active relays, e.g., the half-duplex relay that suffers from low spectral efficiency as well as the FD relay that requires sophisticated techniques for self-interference cancellation.
Fourth, interference suppression becomes effective by utilizing intelligent surfaces which results in a better signal quality for the cell-edge users (Ma et al.
2021
). For multi-user wireless networks, the resources of intelligent surfaces can be allocated over the time/spatial domains to assist data transmissions of different users. In this case, better quality-of-service (QoS) provisioning can be provided to improve the sum-rate performance or max-min fairness among different users.
Fifth, the effective network coverage can be extended by utilizing intelligent surfaces to establish virtual line-of-sight (LoS) links and bypass signal blockages between the transceivers. Based on this, such virtual LoS links establishment can also offer new opportunities to achieve localization in blind spots (Aubry et al.
2021
). In this case, intelligent surfaces can be seamlessly integrated into existing wireless systems to support ubiquitous connectivity and provide great flexibility for sensing and communication.
Finally, the intelligent surface is made of low-cost passive scattering elements, which is easier to be deployed and more sustainable to be operated compared to active nodes (e.g., APs, BSs, and relays) since the intelligent surfaces can be battery-less and wirelessly powered by RF-based energy harvesting. Moreover, it can be easily attached to and removed from the facades of buildings, indoor walls, ceilings, and even mobile vehicles/trains.
Motivated by the above advantages, it is foreseen that intelligent surfaces will bring fundamental paradigm shifts in wireless network design in the future. For example, as compared to employing an M-MIMO antenna array with an extremely large size, it is more sustainable and energy-efficient for the new small/moderate-scale MIMO networks assisted by intelligent surfaces. As such, different from M-MIMO leveraging tens and even hundreds of active antennas to generate sharp beams steering toward users' direction, intelligent surface-assisted MIMO systems can create fine-grained reflect/refract beams via smart passive reflection/refraction design by exploiting the large aperture of intelligent surfaces, which only requires substantially fewer antennas while satisfying the users' QoS requirements. Thus, this significantly reduces the system hardware cost and energy consumption, especially for wireless systems migrating to higher frequency bands in next-generation wireless networks. On the other hand, while existing wireless networks rely on a heterogeneous multi-layer architecture consisting of macros and small BSs/APs, they are active nodes that generate new signals. Therefore, complex coordination and interference management between these nodes are required to achieve the premise of network space capacity enhancement. However, this approach inevitably increases network operating overhead, and it may not be cost-effective to sustain the growth of wireless network capacity in the future. Differently, integrating intelligent surfaces into wireless networks creates a new hybrid architecture comprising active and passive components intelligently. Since smart surfaces are much lower cost than their active counterparts, they can be deployed more densely in wireless networks at a lower cost without the need for sophisticated interference management. By optimally designing the ratio between active BSs and passive intelligent surfaces deployed in a hybrid network, sustainable network capacity scaling with cost can be achieved (Lyu and Zhang 2021).
1.4 Potential Applications
Figure 1.2 illustrates an envisioned future wireless network assisted by intelligent surfaces with a variety of promising applications, including service enhancement in the dead zone, smart office, smart industry, and smart transportation. Specifically, in outdoor environments, the intelligent surface can be coated on building facades, lampposts, billboards, and even the surfaces of high-speed vehicles. By effectively compensating for the Doppler effect (Basar 2021), intelligent surfaces can support smart cities, intelligent transportation, and other applications. On the other hand, in indoor environments, intelligent surfaces can also be attached to the ceilings, walls, furniture, and even behind the paintings/decorations to effectively tackle the issues of limited or unavailable coverage caused by occlusions and to achieve high-capacity hot-spot for eMBB and mMTC applications,1 thereby providing low-cost and easy-to-implement coverage enhancement solutions for manufacturing and remote control. Overall, intelligent surfaces are a disruptive technology that can make our current passive environment intelligent, active, and controllable, and it may benefit a wide range of 5G/6G vertical industries. Furthermore, there is a high interest in implementing and commercializing intelligent surface-like technologies to create new valuable industry chains, and several pilot projects about intelligent surfaces have been launched to advance research in this new area. For example, NTT DOCOMO and Metawave company demonstrated a 28 GHz band based on the constructed meta-structure reflectarray in 2018 (Chen et al. 2021). Moreover, Greenerwave company developed physics-inspired algorithms for reconfigurable metasurfaces. In September 2020, ZTE Company joined hands with more than ten domestic and foreign enterprises and universities, including China Unicom, to establish the “RIS Research Project” in CCSA TC 5-WG 6 (Liu et al. 2022). In September 2021, the IMT-2030 (6G) Promotion Group officially released the industry's first research report on smart metasurface technology at the 6G seminar (Wang et al. 2022).
Figure 1.2 Smart city empowered by intelligent surfaces.
Aiming at the main applications of intelligent surfaces in future wireless networks, several typical scenarios are shown in Fig. 1.3. For example, for a user located in a service shadow zone in Fig. 1.3(a), intelligent surfaces can be deployed in a proper location to create a virtual LoS link between the user and its serving BS. This is especially useful for coverage extension of mmWave and THz communications that are highly susceptible to obstruction. In addition, deploying intelligent surfaces at the cell edge not only helps increase the expected signal power of users at the cell edge but also helps suppress co-channel interference from adjacent cells to users, as shown in Fig. 1.3(b). Similarly, intelligent surfaces play an important role in secure communications by controlling the potential signal leakage energy at the eavesdropper's location, c.f. Fig. 1.3(c). More specifically, when the link distance from the BS to the eavesdropper is smaller than that to the legitimate user, or the eavesdropper lies in the same direction as the legitimate user, the achievable secrecy communication rates are highly limited (even by employing transmit beamforming at the BS in the latter case). However, if an intelligent surface is deployed in the vicinity of the eavesdropper, the reflected signal from the intelligent surface can be adapted to cancel the signal from the BS at the eavesdropper, thus effectively reducing the information leakage. Besides improving communication performance, multi-antenna radar can also utilize intelligent surfaces to increase the signal power reflected from the target in Fig. 1.3(d). In particular, intelligent surfaces can also help to obtain the target information by establishing artificial virtual LoS links from the radar to the targets. Furthermore, to improve the efficiency of simultaneous wireless information and power transfer (SWIPT) from BSs/APs to wireless devices (Wu and Zhang 2020), the large aperture of the intelligent surface can be exploited to compensate for significant power loss at long distances by reflection/refraction to its nearby counterparts, as shown in Fig. 1.3(e).
Figure 1.3 Illustration of intelligent surface applications.
In addition to the basic applications mentioned above, several new trends in intelligent surface applications are being investigated, including the transition from single surfaces to network-level surfaces, from passive surfaces to active and hybrid surfaces, and from reflective/refractive surfaces to omni-surfaces, from ground surfaces to integrated air-ground surfaces, from fixed surfaces to mobile surfaces, from simple connection surface to beyond diagonal surfaces, and so on.
The rest of this book is organized as follows. Part I includes another three chapters to illustrate several key aspects of the system design of intelligent surfaces in detail, such as the intelligent surface architecture and hardware design in Chapter 2, the channel modeling methods for intelligent surfaces in Chapter 3, as well as the associated main challenges and solutions in Chapter 4. Furthermore, Part II focuses on several promising designs for intelligent surface empowered 6G wireless systems. First, Chapter 5 provides an overview of intelligent surfaces for 6G and industry advance, followed by typical combing with conventional techniques, such as IRS for massive MIMO/OFDM in Chapter 6; IRS design for URLLC and sensing/localization in Chapters 7 and 8, respectively; besides these conventional techniques, IRS-aided mmWave/THz communications, IRS-aided NOMA, interference nulling assisted by IRS are investigated in Chapters 9, 10, and 12, respectively; except for communication, intelligent surfaces can also be designed for assisting edge computing and machine learning, which are studied in Chapters 11 and 13, respectively. Moreover, in Part III, we discuss other relevant topics on intelligent surfaces for broadening their scopes, e.g., Chapter 14 discusses the key problem of IRS-aided wireless power transfer and energy harvesting; Chapter 15 presents the advantages of IRS for physical layer security design; finally, the potential for wireless optical communication are also investigated.
1.5 Conclusion
In this chapter, a comprehensive introduction to the new intelligent surface technology is provided. A detailed concept of intelligent surfaces and the corresponding promising applications are presented to overview existing related works and inspire future direction. Finally, the organizational structure of the book is briefly explained.
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Note
1
Note that such surfaces can actually be fabricated as mirrors or lenses for signal reflection and refraction, respectively, depending on the application scenario where the wireless transmitter and receiver are located on the same or opposite sides of the surface.
2IRS Architecture and Hardware Design
Zijian Zhang, Yuhao Chen, Qiumo Yu, and Linglong Dai
Department of Electronic Engineering, Tsinghua University, Beijing, China
2.1 Metamaterials: Basics of IRS
In a broad sense, IRS is a branch of electromagnetic metamaterials. Specifically, metamaterials can be divided into three-dimensional metamaterials and two-dimensional metasurfaces (Di Renzo et al. 2020). The widely studied metasurfaces can be divided into the metasurfaces with fixed physical parameters and those with adjustable electromagnetic properties.
Generally, IRS is a kind of dynamically adjustable metasurface. Metamaterials were first known as “left-hand materials” and “double-negative electromagnetic media.” In 1967, Viktor Veselago published a Russian paper on metamaterials (Veselago 1967), which was translated into English in 1968. In this paper, Veselago first proposed the concept of left-hand materials, which refer to the materials with negative dielectric constant and negative permeability . He also systematically analyzes the propagation characteristics of electromagnetic waves in double-negative media, and predicts several novel anomalous electromagnetic phenomena in theory. In 1996, John B. Pendry verified the existence of negative permittivity (Pendry et al. 1996), and he further proposed the realization of periodic arrangement and verified the negative permeability (Pendry et al. 1999). The earliest research on artificial metasurfaces was the mushroom-shaped high-impedance surface (HIS) proposed by the team leaded by Daniel F. Sievenpiper in Sievenpiper et al. (1999).
The electromagnetic properties of three-dimensional metamaterials can be described by using traditional equivalent medium parameters (e.g., dielectric constant and permeability), but these parameters are no longer applicable to the analysis of metasurfaces (Cui et al. 2014). Regarding the two-dimensional structure of metasurfaces, researchers have successively proposed a variety of theories for analysis and modeling, among which the most representative is the generalized Snell's law proposed by Federico Capasso's team in 2011 (Yu et al. 2011). It is proved that the generalized Snell's law can well describe the physical properties of electromagnetic metasurfaces, which has become a common method for analysis and modeling for a long time.
2.2 Programmable Metasurfaces
The early metasurfaces are designed and fabricated based on the metamaterials. When the physical structure of these metasurfaces is determined, their functional performances are fixed. Since these metasurfaces do not support dynamic adjustments, the flexibility of their applications is limited. To solve this problem, the programmable metasurface has become a mainstream of researches. Various tunable metamaterials have been used to implement programmable metasurfaces, as shown in Fig. 2.1. Particularly, as a representative realization, active components (such as switching diodes and varactors), or adjustable materials (such as graphene) are widely integrated into the programmable metasurfaces. Through adjusting the external excitation, the fixed physical structure of the metasurfaces shows reconfigurable electromagnetic characteristics, which provides a possibility to implement a practical IRS.
In the early research, electromagnetic metasurfaces usually use continuous or quasi-continuous polarizability, impedance, amplitude, phase, and other parameters to characterize the electromagnetic characteristics on their interfaces. These characterization methods are used to design metasurfaces from a perspective of physics; thus, these metasurfaces can be called “analog metasurfaces,” In 2014, Tiejun Cui of Southeast University put forward the concept of “digital coding and programmable metasurfaces,” and used the form of binary coding to characterize the electromagnetic characteristics of metasurfaces (Cui et al. 2014). After modeling the adjustable physical properties, the mature coding algorithm and software in computer can be used to optimize the physical parameters of metasurfaces. Besides, it is also convenient to better use artificial intelligence (AI) to intelligently reconfigure the electromagnetic responses of metasurfaces (Ma et al. 2019). In 2017, Cui's team published a paper to summarize the existing research and propose the concept of “information metasurfaces,” which introduces well-known IRS technology (Cui et al. 2017).
Figure 2.1 Programmable metasurfaces made of different stimuli-sensitive metamaterials. (a) Electric-sensitive liquid crystal-based metasurface. (b) Magnetic sensitive ferrite-based metasurface. (c) Light-sensitive semiconductor-based metasurface. (d) Thermal-sensitive VO2-based metasurface.
Source: Fu Liu et al. 2018/Reproduced from IEEE.
2.3 IRS Hardware Design
2.3.1 IRS System Architecture
Whether it is used as a terminal antenna or a control device for a channel, the hardware architecture of a smart metasurface consists of three main parts: a reconfigurable electromagnetic surface, a feeding system, and a control system.
The reconfigurable electromagnetic surface is the main part of the system to control the space wave. Its structure is an array composed of periodic or quasi-periodic surface elements. Each element integrates nonlinear devices such as PIN diodes (Mamedes et al. 2020), varactors (Rains et al. 2022) or MEMS switches (Baghchehsaraei and Oberhammer 2013). Generally speaking, these nonlinear devices respond to low-frequency control signals given by the control system, changing the electromagnetic properties of the local elements, thereby regulating the high-frequency signals from the feeding system (Fig. 2.2).
The feeding system provides energy and controls the communication signals of the entire system. According to the way of feeding and output, it can be divided into reflection type (Dai et al. 2018), transmission type (Asadchy et al. 2016), active and passive integrated type (Venkatesh et al. 2022), and near-field transmission type. Its function is to input the electromagnetic wave to be modulated onto the electromagnetic surface. Among them, the far-field reflective and transmissive feeding systems can be actively transmitted by the feed antenna in the same system, or can passively receive long-range electromagnetic waves from other signal sources. At this time, there will be no physical entities in the feed system, but still an important part of the overall system.
Figure 2.2 Four typical types of feeding system.
Figure 2.3 1-bit IRS element. (a) Perspective view. (b) Side view. (c) Simulated reflection magnitude and phase.
The control system is usually integrated on the field programmable gate array (FPGA) or other programmable platforms to control the electromagnetic property of the surface. Control signal on low frequency is generated on it based on control decision of higher-level system, adapting the voltage on the nonlinear devices on the electromagnetic surface, so that the property of the surface can be controlled in real time (Fig. 2.3).
2.3.2 IRS Element Design
IRS element design is the core of IRS design. The design goals need to be determined based on the actual application requirements. The main part of the element and bias circuit are then carefully designed and optimized.
