Intelligent Reconfigurable Surfaces (IRS) for Prospective 6G Wireless Networks E-Book
100,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: IEEE ComSoc Pocket Guides to Communications Technologies
- Sprache: Englisch
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.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 468
Veröffentlichungsjahr: 2022
Ähnliche
Table of Contents
Cover
Title Page
Copyright
List of Contributors
1 Introduction
References
2 IRS in the Near‐Field: From Basic Principles to Optimal Design
2.1 Introduction
2.2 Basic Principles
2.3 Near‐Field Channel Model
2.4 Phase Shift Design
2.5 Energy Efficiency
2.6 Optimal IRS Placement
2.7 Open Future Research Directions
2.8 Conclusions
References
Notes
3 Feasibility of Intelligent Reflecting Surfaces to Combine Terrestrial and Non‐terrestrial Networks
3.1 Introduction
3.2 Intelligent Reflecting Surfaces
3.3 Non‐terrestrial Networks
3.4 Revamping Non‐terrestrial Networks Using Intelligent Reflecting Surfaces
3.5 Conclusion
References
4 Towards the Internet of MetaMaterial Things: Software Enablers for User‐Customizable Electromagnetic Wave Propagation
4.1 Introduction
4.2 Pre‐requisites and Related Work
4.3 Networked meta‐materials and SDN workflows
4.4 Application Programming Interface for Meta‐materials
4.5 The Meta‐material Middleware
4.6 Software Implementation and Evaluation
4.7 Discussion: The Transformational Potential of the IoMMT and Future Directions
4.8 Conclusion
Acknowledgements
References
Notes
5 IRS Hardware Architectures
5.1 Introduction
5.2 Concept, Principle, and Composition of IRS
5.3 Operation Mode of IRS
5.4 Hardware Configuration of IRS
5.5 Conclusions
References
6 Practical Design Considerations for Reconfigurable Intelligent Surfaces
6.1 Intelligent Reflecting Surface Architecture
6.2 Physical Limitations of IRSs
References
7 Channel Modelling in RIS‐Empowered Wireless Communications
7.1 Introduction
7.2 A General Perspective on RIS Channel Modelling
7.3 Physical Channel Modelling for RIS‐Empowered Systems at mmWave Bands
7.4 Physical Channel Modelling for RIS‐Empowered Systems at Sub‐6 GHz Bands
7.5 SimRIS Channel Simulator
7.6 Performance Analysis Using SimRIS Channel Simulator
7.7 Summary
Funding Acknowledgment
References
8 Intelligent Reflecting Surfaces (IRS)‐Aided Cellular Networks and Deep Learning‐Based Design
8.1 Introduction
8.2 Contributions
8.3 Literature Review
8.4 System Model
8.5 Problem Formulation
8.6 Phase Shifts Optimization
8.7 Numerical Results
8.8 Conclusion
References
Note
9 Application and Future Direction of RIS
9.1 Background
9.2 Introduction
9.3 RIS‐assisted High‐Frequency Communication
9.4 RIS‐assisted RF Sensing and Imaging
9.5 RIS‐assisted‐UAV Communication
9.6 RIS‐assisted Wireless Power Transfer
9.7 RIS‐assisted Indoor Localization
9.8 Conclusion
References
10 Distributed Multi‐IRS‐assisted 6G Wireless Networks: Channel Characterization and Performance Analysis
10.1 Introduction
10.2 System Model
10.3 Channel Characterization and Performance Analysis
10.4 Numerical Results and Discussions
10.5 Conclusions
References
11 RIS‐Assisted UAV Communications
11.1 Introduction
11.2 Background
11.3 The Role of UAVs in the Future Mobile Networks and Their Unique Characteristics
11.4 Challenges of UAV Communications
11.5 RIS‐assisted UAV Communications: Integration Paradigms and Use Cases
11.6 Preliminary Investigations
11.7 Conclusions
References
12 Optical Wireless Communications Using Intelligent Walls*
12.1 Introduction
12.2 Optical IRS: Background and Applications
12.3 Case Study: High Performance IRS‐Aided Indoor LiFi
12.4 Challenges and Research Directions
References
Note
13 Conclusion
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Nomenclature adopted in this chapter.
Table 2.2 Fresnel region versus IRS size at
GHz.
Chapter 3
Table 3.1 NTN reference scenarios.
Chapter 7
Table 7.1 An overview of RIS channel modelling studies and their main contr...
Table 7.2 System configurations RIS‐assisted MIMO channel modelling.
Table 7.3 System parameters of physical RIS‐assisted channel modelling for ...
Table 7.4 System configurations and simulation parameters.
Chapter 8
Table 8.1 Optimization literature review
Table 8.2 PPO hyperparameters
Table 8.3 DDPG hyperparameters
Table 8.4 Inference time in ms
Chapter 10
Table 10.1 Simulation parameters.
Chapter 12
Table 12.1 Details of the parameters used in the MCRT simulations.
Table 12.2 Details of the VL band optical channel parameters when IRSs are ...
Table 12.3 Details of the IR band optical channel parameters when IRSs are ...
List of Illustrations
Chapter 2
Figure 2.1 Phase and amplitude of the reflection coefficient versus
for
Figure 2.2 Illustration of the IRS geometry used in the channel model.
Figure 2.3 Squared magnitude of the scattered field versus observation angle...
Figure 2.4 Illustration of the parallel‐ray approximation in the far‐field r...
Figure 2.5 Normalized power gain versus distance
for an
‐element IRS with...
Figure 2.6 Normalized power gain versus number
of IRS elements for
,
, a...
Figure 2.7 Power gain versus number
of IRS elements for
,
m, and
.
Figure 2.8 Achievable rate and EE versus distance
for
and a fixed IRS lo...
Figure 2.9 Number of IRS elements versus distance
for
and a fixed IRS lo...
Figure 2.10 Number
of IRS elements versus distance
for
and a varying I...
Chapter 3
Figure 3.1 Potential architecture of enabling a typical mobile handset to di...
Figure 3.2 Illustration of a typical IRS to aid the transmissions between th...
Figure 3.3 Illustration of different NTN platforms.
Figure 3.4 Use cases of NTN, using LEO satellite constellations.
Figure 3.5 Hierarchical representation of NTCS.
Figure 3.6 Transparent satellite‐based NTN architecture.
Figure 3.7 Regenerative‐based satellite NTN architecture with distributed un...
Figure 3.8 Regenerative‐based satellite NTN architecture with gNB on satelli...
Figure 3.9 Comparison of energy efficiency versus data rate for IRS and deco...
Chapter 4
Figure 4.1 Networked meta‐material structure and possible energy wave intera...
Figure 4.2 The programmable wireless environment introduced in [20] is creat...
Figure 4.3 Envisioned applications of the IoMMT in smart houses and products...
Figure 4.4 Energy manipulation domains of artificial materials: (a) electrom...
Figure 4.5 Overview of the metasurface/metamaterial structure and operating ...
Figure 4.6 SDN schematic display of the system model and the entire workflow...
Figure 4.7 (a) Case diagram of the main functions supported by the three bas...
Figure 4.8 A metamaterial Function Deployment request initiates an API callb...
Figure 4.9 The Meta‐material Middleware functionality optimization workflow....
Figure 4.10 Workflow for profiling a meta‐material functionality. The workfl...
Figure 4.11 (a) Snapshots of the Meta‐material Middleware GUI during the met...
Figure 4.12 (a) Element states and far‐field scattering diagram of a 4‐bit m...
Figure 4.13 Arbitrary functionality optimization test. A smiley face‐shaped ...
Figure 4.14 Envisioned future research directions for the Internet of MetaMa...
Chapter 5
Figure 5.1 (a) Conceptional illustration of IRS (1‐bit digital coding meta‐s...
Figure 5.2 (a) Scattering beams of IRS with different spatial coding sequenc...
Figure 5.3 (a) Conceptional illustration of the RFocus prototype.
Figure 5.4 (a) Illustration of the IRS‐aided wireless communication prototyp...
Figure 5.5 (a) Schematic of the wireless communication architecture based on...
Figure 5.6 Experimental environments and results of the proof‐of‐concept pro...
Figure 5.7 Hardware configuration of IRS.
Chapter 6
Figure 6.1 Comparison of the hardware structures of generic meta‐surfaces (a...
Figure 6.2 Front view of an example intelligent reflecting surface consistin...
Figure 6.3 An example outline of a control layer for intelligent reflecting ...
Figure 6.4 Digital meta‐surface unit cell employing three PIN diodes as acti...
Figure 6.5 Reflection phase versus magnitude for the three PIN diode‐based r...
Figure 6.6 Reflection phase (solid curves) and magnitude (dashes curves) for...
Figure 6.7 Plot showing equivalent bit number for the digital meta‐surface u...
Figure 6.8 Plot showing average reflection magnitude for the digital meta‐su...
Chapter 7
Figure 7.1 Miscellaneous RIS‐assisted transmission scenarios and application...
Figure 7.2 Classification of the channel and propagation modelling approache...
Figure 7.3 Generic InH Indoor Office environment with
clusters between Tx–...
Figure 7.4 The considered UMi Street Canyon outdoor environment with random ...
Figure 7.5 Summary of the major steps of RIS‐assisted physical channel model...
Figure 7.6 Generic system model for an RIS‐assisted network with
number of...
Figure 7.7 The major generation steps of the Tx–RIS channel in RIS‐assisted ...
Figure 7.8 Graphical user interface (GUI) of the SimRIS Channel Simulator v2...
Figure 7.9 Top view of the considered transmission scenario with seven refer...
Figure 7.10 Achievable rate analysis in the presence of an RIS for changing
Figure 7.11 Achievable rate analysis of MIMO communication system with varyi...
Chapter 8
Figure 8.1 System model for direct and IRS‐assisted communication in multi‐I...
Figure 8.2
Worst‐case scenario
: Comparison of the user achievable rate ...
Figure 8.3
Random scenario
: Comparison of the user achievable rate and the as...
Figure 8.4
Optimal scenario
: Comparison of the user‐achievable rate and the a...
Chapter 9
Figure 9.1 Evolution of intelligent reflective surface.
Figure 9.2 2D layout of the RIS elements along
x
‐ and
y
‐axis.
Figure 9.3 Different RIS deployment scenarios.
Figure 9.4 Various scenarios of RF‐assisted health care application.
Figure 9.5 RIS multi‐communication scenarios.
Chapter 10
Figure 10.1 Schematic illustration of the considered distributed multi‐IRS‐a...
Figure 10.2 The simulation true CDF and the obtained analytical CDFs of
in...
Figure 10.3 Graphical demonstration of the simulation true PDF and the obtai...
Figure 10.4 Comparison of the lower tails of the Gamma PDF (dashed curves) a...
Figure 10.5 The OP as a function of transmit power
(dBm), where the co‐cha...
Figure 10.6 The EC as a function of transmit power
(dBm), where the co‐cha...
Chapter 11
Figure 11.1 UAV dynamic deployment.
Figure 11.2 LOS probability.
Figure 11.3 Relationship between path loss and LOS probability to the UAV al...
Figure 11.4 RIS‐assisted transmission.
Figure 11.5 RIS‐assisted UAV implementation scenarios.
Figure 11.6 RIS/relay‐assisted UAV communication system.
Figure 11.7 The simulation setup for RIS/relay‐assisted UAV communication sy...
Figure 11.8 The achievable rate for different transmission modes as a functi...
Figure 11.9 The transmit power needed to achieve a rate of
bit/s/Hz as a f...
Chapter 12
Figure 12.1 General architecture of an IRS [5].
Figure 12.2 Meta‐surfaces light manipulation functionalities.
Figure 12.3 Block diagram for MCRT‐based LiFi channel modelling environment....
Figure 12.4 Isometric view of the considered scenario. The global origin poi...
Figure 12.5 Relative radiometric colour spectrum of; (a) VL
and (b) IR
b...
Figure 12.6 Source directivity plots of the VL (a) and IR (b) band LED chips...
Figure 12.7 The material that is used in our simulations [62]: Cobalt green ...
Figure 12.8 Relative spectral reflectivity values of the coating materials, ...
Figure 12.9 Relative spectral reflectivity values of the chosen materials wi...
Figure 12.10 The relative spectral response curves for the adopted detectors...
Figure 12.11 Relative angular responsivity characteristic plots for the adop...
Figure 12.12 Top view of the considered scenario with transmitter (TX1, TX2,...
Figure 12.13 IRS‐aided indoor LiFi CIR simulation results for VL band. The r...
Figure 12.14 IRS‐aided indoor LiFi CIR simulation results for IR band. The r...
Figure 12.15 Achievable capacity plots for (a) VL band and (b) IR band indoo...
Guide
Cover
Table of Contents
Title Page
Copyright
List of Contributors
Begin Reading
Index
End User License Agreement
Pages
ii
iii
iv
xiii
xiv
xv
xvi
xvii
1
2
3
4
5
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
131
130
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
259
258
257
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
279
280
281
282
283
284
IEEE Press
445 Hoes Lane Piscataway, NJ 08854
IEEE Press Editorial Board
Sarah Spurgeon,
Editor in Chief
Jón Atli Benediktsson
Andreas Molisch
Diomidis Spinellis
Anjan Bose
Saeid Nahavandi
Ahmet Murat Tekalp
Adam Drobot
Jeffrey Reed
Peter (Yong) Lia
Thomas Robertazzi
Intelligent Reconfigurable Surfaces (IRS) for Prospective 6G Wireless Networks
Edited by
Muhammad Ali ImranJames Watt School of Engineering, University of Glasgow, Glasgow, UK
Lina MohjaziJames Watt School of Engineering, University of Glasgow, Glasgow, UK
Lina BariahTechnology Innovation Institute, Abu Dhabi, UAE
Sami MuhaidatKU Center for Cyber‐Physical Systems, Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, UAE
Tie Jun CuiState Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China
Qammer H. AbbasiJames Watt School of Engineering, University of Glasgow, Glasgow, UK
Copyright © 2023 by the IEEE Computer Society. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per‐copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750‐8400, fax (978) 750‐4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748‐6011, fax (201) 748‐6008, or online at http://www.wiley.com/go/permission.
Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates in the United States and other countries and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book.
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762‐2974, outside the United States at (317) 572‐3993 or fax (317) 572‐4002.
Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com.
Library of Congress Cataloging‐in‐Publication Data Applied for:
Hardback ISBN: 9781119875253
Cover Design: WileyCover Image: © natrot/Shutterstock
List of Contributors
Qammer H. Abbasi
James Watt School of Engineering
University of Glasgow
Glasgow UK
Mohammad O. Abualhauja'a
James Watt School of Engineering
University of Glasgow
UK
Hanaa Abumarshoud
James Watt School of Engineering
University of Glasgow
UK
Ian F. Akyildiz
Truva Inc.
Georgia
USA
Shuja Ansari
James Watt School of Engineering
University of Glasgow
UK
Stylianos D. Assimonis
ECIT Institute
Centre for Wireless Innovation
Queen's University Belfast
Northern Ireland
UK
Lina Bariah
Technology Innovation Institute
Masdar City
Abu Dhabi
UAE
Ertugrul Basar
Department of Electrical and Electronics Engineering
Koç University
Sariyer, Istanbul
Turkey
Qiang Cheng
State Key Laboratory of Millimeter Waves
Southeast University
Jiangsu Province, Nanjing
China
and
Institute of Electromagnetic Space
Southeast University
Jiangsu Province, Nanjing
China
and
Frontiers Science Center for Mobile Information Communication and Security
Southeast University
Nanjing, China
Michail Christodoulou
Electrical and Computer Engineering Department
Aristotle University
Thessaloniki
Greece
Tie Jun Cui
State Key Laboratory of Millimeter Waves
Southeast University
Jiangsu Province, Nanjing
China
and
Institute of Electromagnetic Space
Southeast University
Jiangsu Province, Nanjing
China
and
Frontiers Science Center for Mobile Information Communication and Security
Southeast University
Nanjing
China
Jun Y. Dai
State Key Laboratory of Millimeter Waves
Southeast University
Jiangsu Province, Nanjing
China
and
Institute of Electromagnetic Space
Southeast University
Jiangsu Province, Nanjing
China
and
Frontiers Science Center for Mobile Information Communication and Security
Southeast University
Nanjing
China
Tri N. Do
The Department of Electrical Engineering
the École de Technologie Supérieure (ÉTS)
Université du Québec
Montréal, QC
Canada
Konstantinos Dovelos
ECIT Institute
Centre for Wireless Innovation
Queen's University Belfast
Northern Ireland
UK
Amal Feriani
Department of Electrical and Computer Engineering
University of Manitoba
Manitoba, Winnipeg
Canada
Harald Haas
LiFi Research and Development Centre (LRDC)
Department of Electronic & Electrical Engineering
The University of Strathclyde
Glasgow
UK
Ekram Hossain
Department of Electrical and Computer Engineering
University of Manitoba
Manitoba, Winnipeg
Canada
Muhammad Ali Imran
James Watt School of Engineering
University of Glasgow
Glasgow
UK
Sotiris Ioannidis
Foundation for Research and Technology – Hellas
Heraklion
Greece
and
School of Electrical and Computer Engineering
Technical University of Chania
Crete
Greece
Muhammad A. Jamshed
James Watt School of Engineering
University of Glasgow
Glasgow
UK
Georges Kaddoum
The Department of Electrical Engineering
the École de Technologie Supérieure (ÉTS)
Université du Québec
Montréal, QC
Canada
Nikolaos Kantartzis
Electrical and Computer Engineering Department
Aristotle University
Thessaloniki
Greece
Jalil R. Kazim
James Watt School of Engineering
University of Glasgow
Glasgow
UK
Christos Liaskos
Computer Science Engineering Department
University of Ioannina
Ioannina
Greece
and
Foundation for Research and Technology – Hellas
Heraklion
Greece
Michail Matthaiou
ECIT Institute
Centre for Wireless Innovation
Queen's University Belfast
Northern Ireland
UK
Lina Mohjazi
James Watt School of Engineering
University of Glasgow
Glasgow
UK
Sami Muhaidat
Department of Electrical Engineering and Computer Science
KU Center for Cyber‐Physical Systems
Khalifa University
Abu Dhabi
UAE
and
Department of Systems and Computer Engineering
Carleton University
Ottawa
Canada
Hien Q. Ngo
ECIT Institute
Centre for Wireless Innovation
Queen's University Belfast
Northern Ireland
UK
Thanh L. Nguyen
The Department of Electrical Engineering
the École de Technologie Supérieure (ÉTS)
Université du Québec
Montréal
QC, Canada
Alexandros Pitilakis
Electrical and Computer Engineering Department
Aristotle University
Thessaloniki
Greece
Andreas Pitsillides
Computer Science Department
University of Cyprus
Nicosia
Cyprus
and
Department of Electrical and Electronic Engineering Science
University of Johannesburg (Visiting Professor)
Johannesburg Gauteng
South Africa
Olaoluwa R. Popoola
James Watt School of Engineering
University of Glasgow
Glasgow
UK
Georgios G. Pyrialakos
Electrical and Computer Engineering Department
Aristotle University
Thessaloniki
Greece
James Rains
James Watt School of Engineering
University of Glasgow
Glasgow
UK
Taniya Shafique
Department of Electrical and Computer Engineering
University of Manitoba
Manitoba, Winnipeg
Canada
Hina Tabassum
Department of Electrical Engineering and Computer Science
York University
Ontario, Toronto
Canada
Ageliki Tsioliaridou
Foundation for Research and Technology – Hellas
Heraklion
Greece
Anvar Tukmanov
BT Labs
Adastral Park
Ipswich
UK
Jalil ur Rehman
James Watt School of Engineering
University of Glasgow
Glasgow
UK
Masood Ur‐Rehman
James Watt School of Engineering
University of Glasgow
Glasgow
UK
Anil Yesilkaya
LiFi Research and Development Centre (LRDC)
Department of Electronic & Electrical Engineering
The University of Strathclyde
Glasgow
UK
Ibrahim Yildirim
Department of Electrical and Electronics Engineering
Koç University
Sariyer, Istanbul
Turkey
and
Faculty of Electrical and Electronics Engineering
Istanbul Technical University
Sariyer, Istanbul
Turkey
Lei Zhang
James Watt School of Engineering
University of Glasgow
Glasgow
UK
1Introduction
Muhammad Ali Imran1, Lina Mohjazi1, Lina Bariah3, Sami Muhaidat2,4, Tei Jun Cui5, and Qammer H. Abbasi1
1 James Watt School of Engineering, University of Glasgow, Glasgow, UK
2 Department of Electrical Engineering and Computer Science, KU Center for Cyber‐Physical Systems, Khalifa University, Abu Dhabi, UAE
3 Technology Innovation Institute, 9639 Masdar City, Abu Dhabi, UAE
4 Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada
5 State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China
The roadmap to beyond the fifth‐generation (B5G) wireless networks is envisaged to introduce a new spectrum of fully automated and intelligent data‐driven services, such as flying vehicles, haptics, telemedicine, augmented and virtual reality, holographic telepresence, and connected autonomous artificial intelligence (AI) systems [1, 2]. Several unprecedented application environments, including machine‐to‐people and machine‐to‐machine communications, are expected to be the driving force of B5G systems. As a result, the number of connected Internet‐of‐Everything (IoE) devices (e.g. sensors, wearables, implantables, tablets) is anticipated to witness a phenomenal growth in the next few years, reaching up to tens of billions [3]. This poses a fundamental challenge on provisioning a ubiquitous seamless connectivity, while concurrently prolonging the lifetime of a massive number of energy‐constrained low‐power, low‐cost devices.
The unprecedented increase of connected devices resulting from the emergence of IoE has created a major challenge for broadband wireless networks, requiring a paradigm shift towards the development of key enabling technologies for the next generation of wireless networks. Fifth‐generation (5G) wireless networks have been identified as the backbone of emerging IoE services and prominently support three use cases: enhanced mobile broadband, ultra‐reliable and low‐latency communications, and massive machine‐type communications. These services are rate‐ and data‐oriented, heterogeneous in nature and defined by a diverse set of key performance indicators. Therefore, enabling them through a single platform while concurrently meeting their stringent requirements in terms of data rate, reliability, and latency is a challenging task [3].
To address these challenges at the physical layer, 5G wireless systems have leveraged the evolution of cutting‐edge technologies, including millimetre wave (mmWave) and terahertz (THz) communications. Although mmWave and THz are highly promising in offering unparalleled data rates and significantly reducing the required device size, their present use is limited due to signal degradation at these extremely high‐communication frequency bands. Moreover, wireless links suffer from attenuation incurred by high propagation loss, high penetration loss, multipath fading, molecular absorption, and Doppler shift [4]. With the lack of full control over the propagation and scattering of electromagnetic (EM) waves, the wireless environment remains unaware of the time‐variant communication, posing fundamental limitations towards building truly pervasive software‐defined wireless networks [5].
Motivated by this, reconfigurable meta‐surfaces, also known as Intelligent Reconfigurable Surfaces (IRS), have emerged as a low‐complexity and energy‐efficient solution that aims at turning the wireless environment into a software‐defined entity. Reconfigurable meta‐surfaces are envisaged to be indispensable in future sixth‐generation (6G) wireless systems due to their potential in realizing massive multiple‐input multiple‐output (MIMO) gains while attaining a notable reduction in energy consumption. The unique design principle of reconfigurable meta‐surfaces lies in realizing artificial structures with massive antenna arrays whose interaction with impinging EM waves can be intentionally controlled through connected passive elements, such as phase shifters, in a way that enhances wireless systems' performance in terms of coverage, rate, and so on, giving rise to the concept of smart radio environments (SREs) [6].
IRS have been deemed as a key contributor in putting down the fundamentals of future 6G networks, and therefore, have attracted a considerable attention from the industrial and academic communities. Therefore, this book will equip the reader with the fundamental knowledge of the operational principles of reconfigurable meta‐surfaces, resulting in its potential applications in various intelligent, autonomous future wireless communication technologies. The opportunities opened by IRS have spurred, in a short span of time, research in many areas related to wireless communication systems. This includes multi‐user resource allocation, beamforming optimization, design of efficient enabling mechanisms, and performance analysis of IRS‐assisted wireless networks.
The aim of this book is to offer the readers the opportunity to explore and comprehend the field of IRS from different angles, including the underlying physics, hardware architecture, operating principles, as well as prototype designs. The book will allow the readers to grasp the knowledge of the interplay of IRS and top‐notch technologies, accompanied by the evolution of 6G networks, with comprehensively studying the advantages, key principles, challenges, and potential use‐cases.
This book is aimed to be a solid foundation for the theoretical investigation and practical implementation of IRS‐enabled wireless networks. The book is envisioned to be a concrete reference for students, researchers, university professors, and industrial people working in the field of intelligent surfaces, in which they can exploit it to identify open research problems, and hence steer their research and industrial activities in those directions. With the diverse aspects studied in our book, we look forward to facilitating a smooth comprehension of the preliminary concepts, as well as providing solid answers to more advanced critical concerns raised in the field of IRS.
Chapter 2 discusses the fundamental principles of IRS‐aided communications and provides an analysis on the near‐field region, wherein the channel modelling and phase shift design problems differ from those in the far‐field. Specifically, the chapter highlights the impact of beamfocusing in manipulating the emitted EM waves to achieve desired signal propagation. This chapter also investigates IRS‐aided MIMO communications and the relationship between the number of reflecting elements and the achieved energy efficiency gains.
In Chapter 3, the potential of deploying IRSs in merging non‐terrestrial networks (NTNs) is explored. This is linked with discussions related to 3GPP standardization guidelines in the context of the various operational aspects, architecture types, and connectivity mechanisms in NTN. Additionally, this chapter highlights how IRSs can be integrated in NTN to enable a typical mobile handset to directly communicate with satellites.
Chapter 4 introduces a new concept called the Internet of MetaMaterial Things (IoMMT), where artificial materials with real‐time tunable physical properties can be interconnected to form a network to realize communication through software‐controlled EM, acoustic, and mechanical energy waves. After exploring the means for abstracting the complex physics behind these materials, their integration into the IoT world is discussed. The chapter presents two novel software categories for the material things, namely the meta‐material Application Programming Interface and Meta‐material Middleware, which will be in charge of the application and physical domain.
Chapter 5 overviews the general hardware architecture of IRS that opened a new platform to dynamically manipulate EM waves. This includes describing the design of an IRS structure based on different categorizations. The available IRS modes of operation will be discussed in deployments relevant to wireless communication systems. The chapter also discusses the hardware aspects and features related to the IRS's main operational principles: reconfigurability, interconnection, computing, networking, programmability, and sensing. This chapter reviews the state‐of‐the‐art on advancements in IRS prototype designs for wavefront manipulation and information modulation.
In Chapter 6, the authors discusses practical design considerations for IRS. Specifically, the tunability of the IRS unit‐cell elements will be explained. Also, the biasing network of an IRS, which provides a means of control over the individual unit cell reflection characteristics, will be detailed. The chapter will provide a comprehensive treatment on the physical limitations of the IRSs including the trade‐off between the bandwidth and phase resolution, the incidence angle response, and the quantization effects.
Chapter 7 explores channel modelling frameworks for facilitating a thorough and accurate evaluation of the system performance of IRS‐aided communications operating in the mmWave and sub‐6 GHz bands. Specifically, the chapter discusses the channel side limitations of the IRS and sheds light on the important role the channel plays in the IRS implementation. The chapter focuses on discussing small‐scale fading and path loss model of IRS‐enabled wireless networks, for different scenarios, including far‐field and near‐field scenarios. Finally, the chapter introduces the open‐source, user‐friendly, and widely applicable SimRIS Channel Simulator v2.0.
Chapter 8 develops an iterative optimization framework to maximize the data rate of a given user by jointly optimizing the user service mode selection along with phase shifts of the nearest IRS of IRS‐assisted users in a large‐scale multi‐user, multi‐base station, multi‐IRS network. This chapter presents semi‐definite programming (SDP)‐based iterative approach for phase optimization, whereas a heuristic approach for mode selection is adopted. In addition, a deep reinforcement learning (DRL) framework is presented with proximal policy optimization (PPO) and double deep policy gradient (DDPG) based solutions to optimize phase shifts.
Chapter 9 investigates the significant role that IRS will play in B5G and 6G wireless networks. Precisely, the chapter discusses the potential of IRS in supporting IRS‐assisted multi‐user communication, IRS‐assisted RF sensing and imaging, IRS‐assisted unmanned aerial vehicle (UAV) communication, IRS‐assisted wireless power transfer, and IRS‐assisted indoor localization. In this context, the authors examine their performance and highlight major performance limiting factors, which open the door for future research directions.
In Chapter 10, the authors study the channel modelling and characterization for multi‐IRS‐assisted wireless systems. For a distributed multi‐IRS (DMI)‐assisted system, in which the IRSs have different geometric sizes and are distributively deployed to aid wireless communications, the authors propose a mathematical framework based on the moment‐matching method to determine the statistical characterization of the end‐to‐end (e2e) channel fading of the DMI system. The obtained approximate distributions are employed to derive tight approximate closed‐form expressions of the outage probability (OP) and ergodic capacity (EC) of the DMI system.
Chapter 11 presents the fundamental characteristics of the UAVs, major paradigms for integrating the UAVs into the wireless networks, and their possible applications, as well as addressing open problems and challenges in the UAV communications. The chapter sheds light on the possible IRS‐assisted UAV systems scenarios. Furthermore, this chapter investigates the performance analysis of the IRS‐assisted UAV systems.
Chapter 12 sets the scene for the integration of IRS with optical wireless communication (OWC), as a promising candidate to support blockage‐free, and therefore, extended coverage communication. The chapter sheds light on the advantages accomplished by leveraging various RIS functionalities in OWC networks, from a transceiver, as well as propagation environment perspectives. The authors present a case study for an IRS‐assisted indoor LiFi system and examine the performance of the considered scenario. The chapter finally highlights some of the challenges and open research directions related to the integration of IRS in OWC.
References
1
Di Renzo, M. Debbah, M. Phan‐Huy, DT et al. (2019). Smart radio environments empowered by reconfigurable AI meta‐surfaces: an idea whose time has come.
EURASIP J. Wireless Commun. Netw.
2019 (1): 1–20.
2
Bariah, L. Mohjazi, L. Muhaidat, S. et al. (2020). A prospective look: key enabling technologies, applications and open research topics in 6G networks.
IEEE Access
8: 174792–174820.
3
Tariq, F. (2020). A speculative study on 6G.
IEEE Wireless Commun.
27 (4): 118–125.
4
Mohjazi, L., Zoha, A., Bariah, L. et al. (2020). An outlook on the interplay of artificial intelligence and software‐defined metasurfaces: an overview of opportunities and limitations.
IEEE Veh. Tech. Mag.
15 (4): 62–73.
5
Liaskos, C., Nie, S., Tsioliaridou, A. et al. (2018). A new wireless communication paradigm through software‐controlled metasurfaces.
IEEE Commun. Mag.
56 (9): 162–169.
6
Basar, E. Di Renzo, M. De Rosny, J. et al. (2019). Wireless communications through reconfigurable intelligent surfaces.
IEEE Access
7: 116753–116773.
2IRS in the Near‐Field: From Basic Principles to Optimal Design
Konstantinos Dovelos, Stylianos D. Assimonis, Hien Q. Ngo and Michail Matthaiou
ECIT Institute, Centre for Wireless Innovation, Queen's University Belfast, Northern Ireland, Belfast, UK
2.1 Introduction
Fifth‐generation wireless technologies, such as massive multiple‐input multiple‐output (MIMO), millimetre wave (mmWave) communication, and ultra‐dense networks, are expected to boost data rates into an unprecedented level [1]. Yet, those technologies rely on the postulate that the wireless channel is uncontrollable, and hence its detrimental effect can be mainly compensated by sophisticated transmission and reception schemes. The advent of meta‐materials, though, has paved the way for the disruptive paradigm of electromagnetic (EM) meta‐surfaces, broadly known as intelligent reconfigurable surfaces (IRSs) [2]. Specifically, an IRS is a planar structure of multiple reconfigurable elements of sub‐wavelength size, which can alter the characteristics of an impinging EM wave. Furthermore, IRSs consist mainly of passive components and hence are more power‐efficient than MIMO relays [3]. Thanks to those unique wave manipulation capabilities, IRSs are reasonably considered by many experts as the ‘next big thing in wireless’ [4]. In this chapter, we will first outline the basic principles behind IRSs and then delve into the specifics of near‐field communication. Finally, we will conclude the chapter by presenting possible avenues for future research. Table 2.1 summarizes the nomenclature used in this chapter.
Table 2.1 Nomenclature adopted in this chapter.
Notation
Description
Expectation operator
Complex Gaussian variable with mean
and variance
Absolute value of a complex number
Argument of a complex number
2.2 Basic Principles
2.2.1 IRS Model
In general, an IRS is modelled as a rectangular array of reflecting elements of sub‐wavelength size, also called unit cells or meta‐atoms. Each unit cell is engineered so that it can alter the phase and amplitude of an impinging EM wave. Particularly, the unit cell usually comprises of a metal patch on the top of a grounded dielectric substrate [5, 6]. Furthermore, a semi‐conductor device1 is embedded into the metal patch to dynamically control the overall element response through a biasing voltage. Owing to the sub‐wavelength size of unit cells, each one can be represented by an equivalent lumped RLC circuit. To this end, the impedance of the th IRS cell is given by [7]
In (2.1), , , , , and are the bottom‐layer inductance, top‐layer inductance, effective capacitance, effective resistance, and angular frequency of the incident wave, respectively. Note that the semi‐conductor device varies the effective capacitance , thereby reflecting the incident wave due to the impedance discontinuity between the free‐space impedance and element impedance . The reflection coefficient of the th unit cell is therefore defined as follows:
Figure 2.1 Phase and amplitude of the reflection coefficient versus for , nH, and nH
As seen from Figure 2.1, the absorption losses are negligible. For this reason, it is assumed that . Then, the reflection coefficient of each unit cell is expressed as , where .
2.2.2 Signal Model of IRS‐Aided System
The IRS is deployed between the transmitter (Tx) and receiver (Rx) to assist communication. Consider that the Tx and Rx are equipped with a single antenna each. The received baseband signal can then be written as follows:
where is the channel from the Tx to the th cell, is the channel from the th cell to the Rx, is the direct channel between the Tx and Rx, denotes the transmitted data symbol with average power , and is the additive noise with power spectral density . Under perfect channel state information at the Rx, the received signal‐to‐noise ratio (SNR) is expressed as follows:
The SNR is maximized when the signals propagating through the IRS and the direct link are coherently combined at the Rx. To achieve this, the phase shift induced by the th element must be
which gives
From (2.6), it is evident that the SNR is larger than that obtained without the IRS. In short, the IRS can boost the signal power at the Rx, which is vital when the direct channel is weak or blocked.
2.3 Near‐Field Channel Model
The channel coefficients depend on the operating frequency and deployment scenario. For example it is customary to assume that and are , i.e. Rayleigh fading, to represent rich scattering in the sub‐6 GHz band [8, 9]. In this chapter, we consider mmWave IRS‐aided channels whose multi‐path scattering is limited [10]. Note that communication in the mmWave band (30–300 GHz) is very attractive for 5G‐and‐beyond networks thanks to the abundant spectrum available at extremely high frequencies [11]. In addition to sparse scattering, an IRS operating at high frequencies is expected to be near the base station so that propagation losses are minimal. Consequently, near‐field effects are of particular relevance. To study those effects, we adopt a geometric channel model that captures the key features of line‐of‐sight (LoS) propagation in the vicinity of the IRS. In particular, we focus on the radiating near‐field of the IRS called Fresnel zone. Recall that the Fresnel zone includes all distances from the IRS satisfying [12]
where and denote the wavelength and maximum IRS dimension, respectively.
From Table 2.2, we evince that a mmWave IRS can have a Fresnel region of tens of metres. Thus, the far‐field assumption of plane waves breaks down as the IRS size grows.
2.3.1 Spherical Wavefront
Both the Tx‐IRS and IRS‐Rx links are LoS. The IRS geometry is depicted in Figure 2.2, where is the area of each unit cell and is the position vector of the th cell. Furthermore, the Tx and Rx position vectors, and , are described in Cartesian coordinates as follows:
Table 2.2 Fresnel region versus IRS size at GHz.
IRS size
Physical IRS size (
)
Fresnel region (m)
Figure 2.2 Illustration of the IRS geometry used in the channel model.
where , , and denote the corresponding radial distances, azimuth angles, and polar angles, respectively. The cascaded channel can now be decomposed as [13]
where is the path loss through the th IRS cell, is the wavenumber, whilst
and
are the distances from the Tx and Rx to the th cell, respectively. Note that the phase variations in (2.10) depend on and , which represent the spherical wavefront of the incident and reflected waves, respectively.
2.3.2 Path Loss
To derive the path loss of the IRS‐aided link, we focus on an arbitrary IRS element and omit the subscript ‘’. The Tx and Rx are in the far‐field of the individual element, which implies that . Thus, a plane wavefront is assumed across the IRS element. For simplicity, we consider a transverse electric incident wave which is linearly polarized along the ‐axis. The electric field of the incident plane wave is expressed as follows:
where denotes the unit vector along the ‐axis. Next, the scattered field at the Rx location is determined by leveraging physical optics [14]. Assuming that the IRS is a perfect electric conductor, the squared magnitude of the scattered electric field is given by [[14], Ch. 13]
where , while and . The approximation in (2.15) follows from and for and , which holds for and . This is also verified in Figure 2.3. Note that each IRS element has sub‐wavelength size in order to act as an isotropic scatterer. The power density of the scattered field at the Rx location is determined as follows:
where the relationship was used, with denoting the antenna gain of the Tx. Considering the Rx aperture gives the received power
Figure 2.3 Squared magnitude of the scattered field versus observation angle for incident angle and scattering plane ; , carrier frequency GHz, and m.
Therefore, the path loss of the Tx‐IRS‐Rx link through the th element is given by
According to (2.18), the path loss of the IRS‐aided link hinges on the reciprocal of the product rather than of the sum , as in the case of specular reflection [15]. As a result, the path attenuation of each cascaded channel is expected to be very high in general.
2.4 Phase Shift Design
In this section, we discuss the phase shift design that maximizes the SNR, and hence the achievable rate, in the near‐field. Specifically, we show that conventional far‐field beamforming can become highly suboptimal when the Tx operates close to the IRS. Instead, the optimal IRS configuration makes use of the spherical wavefront to focus the incident EM wave towards a specific point of space, a capability that is not possible with beamforming.
2.4.1 Beamfocusing
Based on the spherical wave model, the phase profile that maximizes the SNR is given by
which is referred to as beamfocusing. This is because the IRS acts like a lens focusing the incident EM wave towards a specific point of space rather than towards a direction [16]. Under beamfocusing, the SNR becomes
and, hence, it increases quadratically with the number of IRS elements.
2.4.2 Conventional Beamforming
Typical IRS processing [17] relies on the far‐field assumption, whereby the spherical wavefront degenerates into a plane wavefront. This enables the use of the parallel‐ray approximations (i.e. Figure 2.4)
Mathematically speaking, (2.21) and (2.22) follow from the first‐order Taylor expansion of (2.11) and (2.12), respectively. In the so‐called beamforming strategy', the phase shifts are designed as follows:
which depend solely on the angular information and . As a result, beamforming can be highly suboptimal in the Fresnel zone. To analytically characterize the reduction in the SNR, we first consider that the IRS is deployed close to the Tx, whilst the Rx is in the far‐field of the IRS.2 Akin to (2.20), the SNR is lower bounded as follows:
Figure 2.4 Illustration of the parallel‐ray approximation in the far‐field region.
where is the normalized power gain defined as follows:
We next exploit the Fresnel approximation of the Tx distance
to recast under the phase shift design (2.23) as follows:
which admits the approximation:
where denotes the Dirichlet sinc function. The validity of the Fresnel approximation (2.26) is shown in Figure 2.5.
In addition, the accuracy of the approximate closed‐form expression (2.28) is evaluated in Figure 2.6, which exhibits a very good match with the exact formula. Most importantly, we observe that beamforming substantially decreases the power gain when the Tx operates in the near‐field of an electrically large IRS. Lastly, capitalizing on (2.28), we have the asymptotic result as . This implies that for a finite yet large number of IRS elements, the total power gain tends to zero as grows, which is demonstrated in Figure 2.7. In conclusion, the decrease in the power gain cannot be compensated by increasing the number of IRS elements, and hence, beamfocusing is the optimal mode of operation in the Fresnel zone.
Figure 2.5 Normalized power gain versus distance for an ‐element IRS with , , , and m.
Figure 2.6 Normalized power gain versus number of IRS elements for , , and m.
Figure 2.7 Power gain versus number of IRS elements for , m, and .
2.5 Energy Efficiency
So far we have restricted our discussion to a single‐antenna Tx and Rx. We now extend our analysis to the MIMO case, where both the Tx and Rx are equipped with multiple antennas. Specifically, we investigate under which conditions IRS‐aided MIMO can outperform MIMO in terms of energy efficiency (EE).
2.5.1 MIMO System
Consider a MIMO system where the Tx and Rx are equipped with and antennas, respectively. The Tx seeks to communicate a single data stream to the Rx through a LoS channel, whose path loss is calculated using the Friss transmission formula:
where . For an adequately small and , far‐field propagation can be assumed. In this case, the LoS channel is rank‐one [[19], Ch. 7]. Hence, analogue beamforming and combining yield the received SNR:
For a hybrid array architecture with a single radio‐frequency chain, the power consumption of the MIMO system is3
where and denote the power consumption values for a phase shifter and a power amplifier, respectively.
2.5.2 IRS‐aided MIMO System
The Tx and Rx perform beamforming and combining to communicate a single stream through the ‐element IRS. Due to the directional transmissions, the Tx–Rx link is very weak, and hence is ignored. The received SNR of the IRS‐aided MIMO system is simply given by
where denotes the path loss of the IRS‐aided link in (2.18) calculated for an arbitrary IRS element. The phase of each IRS element is controlled by a varactor diode, which consumes a negligible power compared to a typical phase shifter. Thus, the power consumption of each reflecting element is set to [20], and the total power expenditure of the IRS‐assisted MIMO system is .
Proposition 2.1 The IRS‐aided MIMO system with and antennas, where is a positive integer, attains a higher SNR than MIMO with and antennas for
Proof: Using (2.30) and (2.32), the IRS‐aided system achieves a higher SNR for , which gives the desired result after basic algebra.
According to Proposition 2.1, we can decrease the number of Tx and Rx antennas by a factor to reduce the power consumption as follows:
while keeping the achievable rate fixed. Consequently, the EE gain with respect to MIMO is approximately equal to . Let denote the transmit bandwidth. The achievable rate is finally calculated as follows:
whilst the EE is given by . In this numerical experimental, we consider antennas for the MIMO system without an IRS. From Figure 2.8, we verify that the IRS‐assisted MIMO system, with
