Massive MIMO for Future Wireless Communication Systems E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Authoritative resource discussing the development of advanced massive multiple input multiple output (MIMO) techniques and algorithms for application in 6G
Massive MIMO for Future Wireless Communication Systems analyzes applications and technology trends for massive multiple input multiple output (MIMO) in 6G and beyond, presenting a unified theoretical framework for analyzing the fundamental limits of massive MIMO that considers several practical constraints. In addition, this book develops advanced signal-processing algorithms to enable massive MIMO applications in realistic environments.
The book looks closer at applying techniques to massive MIMO in order to meet practical network constraints in 6G networks, such as interference, pathloss, delay, and traffic outage, and provides new insights into real-world deployment scenarios, applications, management, and associated benefits of robust, provably secure, and efficient security and privacy schemes for massive MIMO wireless communication networks.
To aid in reader comprehension, this book includes a glossary of terms, resources for further reading via a detailed bibliography, and useful figures and summary tables throughout.
With contributions from industry experts and researchers across the world and edited by two leaders in the field, Massive MIMO for Future Wireless Communication Systems includes information on:
- Signal processing algorithms for cell-free massive MIMO systems and advanced mathematical tools to analyze multiuser dynamics in wireless channels
- Bit error rate (BER) performance comparisons of different detectors in conventional cell-free massive MIMO systems
- Enhancement of massive MIMO using deep learning-based channel estimation and cell-free massive MIMO for wireless federated learning
- Low-complexity, self-organizing, and energy-efficient massive MIMO architectures, including the prospects and challenges of Terahertz MIMO systems
Massive MIMO for Future Wireless Communication Systems is an essential resource on the subject for industry and academic researchers, advanced students, scientists, and engineers in the fields of MIMO, antennas, sensing and channel measurements, and modeling technologies.
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
Dedication
About the Editors
List of Contributors
Preface
Acknowledgments
1 Fundamentals of Wireless Communications: Massive MIMO Essentials for 6G and Beyond
1.1 Introduction
1.2 Digital and Analog Sources
1.3 Deterministic and Random Waveforms
1.4 Propagation of Electromagnetic Waves
1.5 Information Measures
1.6 Channel and Information
1.7 Modulation and Demodulation
1.8 Massive MIMO
1.9 Security and Privacy of Wireless Systems
1.10 Conclusion
References
2 Security and Privacy of Future Wireless Communication Systems
2.1 Introduction
2.2 Overview of the Current State of Massive MIMO Wireless Communication Systems
2.3 Related Reviews
2.4 Security and Privacy Challenges of Future Wireless Communication Systems
2.5 Physical Layer Security and Technologies for Massive MIMO
2.6 Privacy-Enhancing Technologies
2.7 Emerging Technologies
2.8 Potential Threats and Challenges of Future Wireless Communication Systems
2.9 Open Research Issues and the Future of Security and Privacy
2.10 Limitations of the Study
2.11 Lessons Learned
2.12 Conclusion and Future Scope
References
3 Applications of Massive MIMO in Wireless Communication Systems
3.1 Introduction
3.2 Evolution from MIMO to Massive MIMO and Role in 5G
3.3 Related Works
3.4 Fundamental Limits
3.5 Benefits of Massive MIMO for 6G and Beyond
3.6 Design and Implementation Challenges for Massive MIMO in 6G
3.7 Enabling Technologies for Massive MIMO in 6G
3.8 Applications of Massive MIMO in Wireless Communication Systems
3.9 Recent Advances and Future Outlook for Massive MIMO in 6G
3.10 Limitations of the Study
3.11 Lessons Learned
3.12 Conclusion and Future Scope
References
4 Cell-Free Massive MIMO Technology and Applications in 6G
4.1 Introduction
4.2 Conventional Cell-Free mMIMO Systems
4.3 Scalable User-Centric Cell-Free mMIMO Systems
4.4 Radio Stripes Cell-Free Systems
4.5 Conclusions and Future Work
Acknowledgment
References
5 Localization in Massive MIMO Networks: From Far-Field to Near-Field
5.1 Introduction
5.2 Far-Field DoA Estimation
5.3 Near-Field DoA and Range Estimation
5.4 Conclusions
References
6 Energy-Efficient Uplink Transmission in RIS-Aided M-MIMO IoT Systems*
6.1 Introduction
6.2 Related Works
6.3 System Model for RIS-Aided M-MIMO
6.4 Definitions on Riemannian Manifolds
6.5 Energy-Efficient Uplink Transmission in RIS M-MIMO IoT Systems
6.6 Conclusion and Research Directions
References
Notes
7 Energy Efficiency in RIS-Aided Massive MIMO and XL-MIMO Communication Systems
7.1 Introduction
7.2 General System Model for RIS-Aided M-MIMO
7.3 Energy Efficiency in RIS-Aided M-MIMO
7.4 Energy Efficiency in Demand-Adaptive XL-MIMO Systems
7.5 Conclusions and Perspective
References
Notes
8 NOMA-Aided Massive MIMO for Next-Generation Networks
8.1 Introduction
8.2 Related Work
8.3 System Model
8.4 Simulations and Discussions
8.5 Conclusion
References
9 Efficient Hybrid Precoding for Millimeter-Wave Massive MIMO-NOMA Systems: A Low-Complexity Approach
9.1 Introduction
9.2 Overview of Basic Precoding Scheme
9.3 Related Work
9.4 Mathematical Model
9.5 Results
9.6 Challenges of Hybrid Precoding and Future Research Scope
9.7 Conclusion
References
10 Intelligent Reflecting Surfaces and Next-Generation Wireless Systems
10.1 Introduction
10.2 Related Work
10.3 Intelligent Reflecting Surfaces Hardware and Architectures
10.4 Intelligent Reflecting Surfaces and the Path Loss Model
10.5 IRS-Empowered Slot Scheduling and Cost-Efficient Reflection Optimization
10.6 Two-Timescale Reflection Pattern Design
10.7 Results and Discussions
10.8 Conclusion
10.9 Future Work
References
Note
11 ABER Performance Evaluation of RIS-Aided Millimeter Wave Massive MIMO System Under 3GPP 5G Channels
11.1 Introduction
11.2 Related Work
11.3 System Model
11.4 Channel Model
11.5 Simulations and Discussions
11.6 Conclusions
References
12 Massive MIMO for Non-terrestrial Wireless Communication Systems
12.1 Introduction
12.2 The Role of Satellites in Future Non-terrestrial Networks
12.3 Signal Processing for Non-terrestrial Network-Based Massive MIMO Systems
12.4 Single-Cell Massive MIMO Linear Precoding Techniques
12.5 Multi-cell Massive MIMO Linear Precoding Techniques
12.6 Security Considerations in a Non-terrestrial Network
12.7 Standards and Interoperability Requirements in a Non-terrestrial Network
12.8 Conclusion and Recommendations
References
13 Artificial Intelligence and Machine Learning for Channel Estimation in Massive MIMO Wireless Communication Systems
13.1 Introduction
13.2 Related Work
13.3 Channel Estimation Using LS and MMSE Estimators
13.4 AI and ML-Based Approaches for Channel Estimation in Massive MIMO System
13.5 FL in Cell-Free Massive MIMO: An Overview
13.6 Protection of Privacy of User Data in Cell-Free Massive MIMO Using FL
13.7 Results and Discussions
13.8 Conclusions
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Evolution of wireless communication systems.
Table 2.2 Vulnerabilities and threats in massive MIMO.
Table 2.3 Review of related work.
Table 2.4 Physical layer security techniques for massive MIMO.
Table 2.5 Security and privacy mechanisms beyond physical layer.
Chapter 3
Table 3.1 Motivations for massive MIMO in 6G networks.
Table 3.2 Summary of related works.
Table 3.3 Key parameters and assumptions for massive MIMO capacity analysis ...
Table 3.4 Comparison of enabling technologies for massive MIMO in 6G.
Table 3.5 Applications of massive MIMO in 6G and associated requirements and...
Table 3.6 Summary of massive MIMO applications.
Table 3.7 Recent advances and milestones for massive MIMO.
Chapter 4
Table 4.1 Conventional CF mMIMO simulation parameters.
Table 4.2 Literature comparison for user-centric cell-free mMIMO systems.
Chapter 5
Table 5.1 Comparison of MUSIC and ESPRIT when there are antennas and the D...
Table 5.2 Comparison of MUSIC and ESPRIT when there are antennas and the D...
Table 5.3 Other widely used localization techniques.
Chapter 6
Table 6.1 Related work addressing the current open issues in EE, SE, and RE ...
Table 6.2 Simulation system parameter values, metrics, and optimization vari...
Table 6.3 Computational complexity regarding running time per iteration, in ...
Chapter 7
Table 7.1 Recent works addressing RE (EE and SE) and related open issues in ...
Table 7.2 List of symbols for the M-MIMO and XL-MIMO systems modeling.
Table 7.3 List of symbols used in the RIS-aided M-MIMO model, Section 7.2.3....
Table 7.4 List of simulation parameters.
Table 7.5 List of simulation parameters.
Table 7.6 Summary of the contributions – key quantitative results.
Chapter 8
Table 8.1 Simulation parameters.
Table 8.2 Rate comparison of mMIMO-NOMA and mMIMO-OMA systems for two differ...
Table 8.3 Rate comparison of mMIMO-NOMA and mMIMO-OMA systems with and witho...
Chapter 9
Table 9.1 Simulation parameters.
Chapter 10
Table 10.1 Comparison of massive MIMO and IRS.
Table 10.2 Literature review of related works.
Chapter 11
Table 11.1 Simulation parameters.
Table 11.2 Comparison of the SNR requirements of RIS-aided MIMO indoors an...
Table 11.3 Comparison of the SNR requirements of RIS-aided MIMO indoors at...
Table 11.4 Comparison of the SNR requirements of RIS-aided MIMO indoors at...
Chapter 12
Table 12.1 Non-terrestrial platforms.
Table 12.2 Frequency band allocations for satellite communication services....
Chapter 13
Table 13.1 Comparison of CML and DML.
Table 13.2 Delay profiles for NR channel models.
Table 13.3 TDLA30 (DS = 30 ns).
Table 13.4 TDLB100 (DS = 100 ns).
Table 13.5 TDLC300 (DS = 300 ns).
Table 13.6 Simulation parameters.
List of Illustrations
Chapter 2
Figure 2.1 Vulnerabilities and threats in massive MIMO wireless communicatio...
Figure 2.2 Privacy challenges in massive MIMO.
Figure 2.3 Security mechanisms.
Chapter 3
Figure 3.1 Evolution of MIMO techniques timeline diagram.
Figure 3.2 Theoretical capacity scaling of massive MIMO.
Figure 3.3 Key benefits of massive MIMO for 6G networks.
Figure 3.4 Applications of massive MIMO in wireless communication systems.
Chapter 4
Figure 4.1 Massive MIMO architectures.
Figure 4.2 Conventional cell-free mMIMO architecture.
Figure 4.3 Cell-free mMIMO transceiver system model.
Figure 4.4 BER performance comparison between the CF work proposed in [31, 3...
Figure 4.5 BER performance comparison between CF and SCs systems.
Figure 4.6 Scalable user-centric cell-free mMIMO system.
Figure 4.7 Cell-free mMIMO with limited fronthaul capacity links.
Figure 4.8 BER performance comparison between the UE–APs association method ...
Figure 4.9 BER performance comparison between two scenarios based on the pro...
Figure 4.10 Radio stripe deployment in stadium.
Chapter 5
Figure 5.1 ULA with antennas.
Figure 5.2 The normalized power spectrum for the MUSIC algorithm when the nu...
Figure 5.3 The normalized power spectra of the MUSIC algorithm when the four...
Figure 5.4 Flowchart of the MUSIC and ESPRIT algorithms for DoA estimation....
Figure 5.5 The normalized power spectra for the 2D MUSIC when there are four...
Figure 5.6 The normalized power spectra when there are four sources located ...
Figure 5.7 The normalized power spectra when there are five sources located ...
Figure 5.8 The normalized power spectra for the modified MUSIC and generaliz...
Chapter 6
Figure 6.1 Passive RIS-aided MU M-MIMO system model with LoS/NLoS BS-IoT dev...
Figure 6.2 Iterative-alternating optimization (i-AO) steps for the proposed ...
Figure 6.3 Uplink transmit power () vs minimum spectral efficiency requirem...
Figure 6.4 Uplink transmit power () vs noise power...
Figure 6.5 Uplink transmit power () vs number of IoT devices () being serv...
Chapter 7
Figure 7.1 Chapter organization: two RE problems, which arise in different M...
Figure 7.2 Temporal frame for TDD transmission by adopting instantaneous and...
Figure 7.3 Three-dimensional (3-D) geometric representation of the BS, UEs, ...
Figure 7.4 Normalized channel gain and channel phase–shift of the determinis...
Figure 7.5 RIS-aided M-MIMO multi-user scenarios illustrating the three link...
Figure 7.6 Average EE against the transmit power budget [dBm] at the BS ()....
Figure 7.7 Average number of antennas against Tx power budget in dBm for fou...
Figure 7.8 Percentage of the transmit power budget dBm utilized against the ...
Figure 7.9 CCDF of the ER for the proposed strategies operating under stat...
Figure 7.10 Diagram of the studied demand-adaptive XL-MIMO system. Under hig...
Figure 7.11 EE as a function of the number of active antennas achieved by th...
Figure 7.12 SR as a function of the number of active antennas achieved by th...
Figure 7.13 EE (left) and SR (right) as a function of the number of active a...
Figure 7.14 EE (left) and SR (right) as a function of the transmit power bud...
Chapter 8
Figure 8.1 Multiuser mMIMO system.
Figure 8.2 Downlink NOMA serving -users.
Figure 8.3 UL mMIMO system.
Figure 8.4 UL mMIMO-NOMA system.
Figure 8.5 UL rate of mMIMO-OMA vs mMIMO-NOMA systems for considering an...
Figure 8.6 UL rate of mMIMO-OMA vs mMIMO-NOMA systems for considering an...
Figure 8.7 UL sum rate of mMIMO-OMA vs mMIMO-NOMA systems considering and
Figure 8.8 UL rate of mMIMO-OMA vs mMIMO-NOMA systems for considering an...
Figure 8.9 UL rate of mMIMO-OMA vs mMIMO-NOMA systems for considering an...
Figure 8.10 UL sum rate of mMIMO-OMA vs mMIMO-NOMA systems considering and...
Figure 8.11 UL rate of mMIMO-OMA vs mMIMO-NOMA systems for considering a...
Figure 8.12 UL rate of mMIMO-OMA vs mMIMO-NOMA systems for considering a...
Figure 8.13 UL sum rate of mMIMO-OMA vs mMIMO-NOMA systems considering and...
Chapter 9
Figure 9.1 Architecture of analog beamforming for mmWave-mMIMO systems.
Figure 9.2 Digital precoder: algorithms.
Figure 9.3 Digital precoder single user.
Figure 9.4 Digital precoder multiuser.
Figure 9.5 Hybrid procoding fully connected.
Figure 9.6 Hybrid procoding subconnected.
Figure 9.7 Generalized block diagram of communication systems with precoding...
Figure 9.8 Block diagram of TH algorithm-based precoder.
Figure 9.9 Spectral efficiency variation with SNR.
Figure 9.10 Energy efficiency variation with SNR.
Chapter 10
Figure 10.1 Comparison of power consumption between 4G and 5G BSs.
Figure 10.2 IRS-enabled new functions: (a) modulate the pure carrier and tra...
Figure 10.3 Array geometry structure of IRS.
Figure 10.4 -port single-connected reconfigurable impedance network, which ...
Figure 10.5 Product-distance path loss model in IRS-assisted mmWave system....
Figure 10.6 IRS-assisted downlink mmWave massive MIMO system.
Figure 10.7 An illustration of GD on Riemannian manifolds.
Figure 10.8 Pictorial illustration of the proposed two-timescale beamforming...
Figure 10.9 The small-timescale beamforming procedure: (a) uplink channel es...
Figure 10.10 Achievable sum-rate vs the numbers of BS antennas and IRS ele...
Figure 10.11 Achievable sum-rate vs the IRS elements of different methods....
Figure 10.12 Normalized eigenvalues of the channel correlation matrix vs the...
Figure 10.13 AASR against temporal correlation coefficient .
Chapter 11
Figure 11.1 Pictorial representation of M-MIMO.
Figure 11.2 RIS-assisted communication.
Figure 11.3 Benefits and challenges of mmWave communication.
Figure 11.4 An RIS-assisted mmWave M-MIMO system for indoor environment.
Figure 11.5 An RIS-assisted mmWave M-MIMO system for outdoor environment.
Figure 11.6 ABER performance of RIS-aided MIMO in indoor at 28 GHz with va...
Figure 11.7 ABER performance of RIS-aided MIMO in indoor at 28 GHz with an...
Figure 11.8 ABER performance of RIS-aided MIMO in outdoor at 28 GHz with v...
Figure 11.9 ABER performance of RIS-aided MIMO in indoor at 73 GHz with va...
Figure 11.10 ABER performance of RIS-MIMO in indoor at 28 GHz with and var...
Chapter 12
Figure 12.1 Non-terrestrial network showing space, air, and ground component...
Figure 12.2 Role of satellites in non-terrestrial networks.
Figure 12.3 Applications of satellites in non-terrestrial networks showing, ...
Figure 12.4 The massive MIMO NTN communication channel showing
m
transmit an...
Figure 12.5 Noncooperative multi-cell downlink channels for massive MIMO.
Figure 12.6 Downlink channels of a massive MIMO non-terrestrial network.
Figure 12.7 Overview of the security architecture for non-terrestrial networ...
Figure 12.8 Reference and UE propagation delay.
Figure 12.9 Preamble for random access in non-terrestrial networks.
Figure 12.10 Timing diagram for a single HARQ process with a transparent sat...
Chapter 13
Figure 13.1 SISO system.
Figure 13.2 NMSE performance of LS and MMSE channel estimation in SISO confi...
Figure 13.3 SIMO system with single UE.
Figure 13.4 NMSE performance of LS and MMSE estimators in SIMO system with
Figure 13.5 SIMO system model with antennas at BS and single antenna UEs...
Figure 13.6 NMSE performance of LS and MMSE estimators for different , and
Figure 13.7 NMSE performance of LS and MMSE estimate of channel in SIMO syst...
Figure 13.8 Comparison of LS and MMSE for different observation interval b...
Figure 13.9 Multi-cell massive MIMO system.
Figure 13.10 NMSE performance of LS estimator in multiuser pilot contaminati...
Figure 13.11 SISO system with frequency-selective fading environment.
Figure 13.12 NMSE performance of LS and MMSE estimators in SISO system in fr...
Figure 13.13 SIMO system in frequency-selective fading environment with sing...
Figure 13.14 NMSE performance of LS and MMSE estimators in wideband channel ...
Figure 13.15 SIMO system with frequency-selective fading environment with ...
Figure 13.16 NMSE error in frequency-selective fading channel with 2 UE and ...
Figure 13.17 Uplink channel estimation of a massive MIMO system.
Figure 13.18 NMSE in OFDM - SISO for TDL (30 ns, 100 ns, and 300 ns) NR chan...
Figure 13.19 Uplink sum rate of massive MIMO system with and without CU cons...
Figure 13.20 Uplink sum rate of massive MIMO system with and without CU cons...
Guide
Cover
Table of Contents
Series Page
Title Page
Copyright
Dedication
About the Editors
List of Contributors
Preface
Acknowledgments
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
Moeness Amin
Jón Atli Benediktsson
Adam Drobot
James Duncan
Ekram Hossain
Brian Johnson
Hai Li
James Lyke
Joydeep Mitra
Desineni Subbaram Naidu
Tony Q. S. Quek
Behzad Razavi
Thomas Robertazzi
Diomidis Spinellis
Massive MIMO for Future Wireless Communication Systems
Technology and Applications
Edited by
Webert Montlouis
Johns Hopkins University, United States
Agbotiname Lucky Imoize
University of Lagos, Nigeria
Copyright © 2025 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.
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This book is dedicated with love and affection to my mother, thank you mom.
Webert Montlouis
In memory of my late grandma, Lucy Omoyeni Sado (Madam LS)
Agbotiname Lucky Imoize
About the Editors
Webert Montlouis (Fellow, IEEE) received his BS, MS, and PhD in electrical and computer engineering from Northeastern University, Boston, MA. He is currently at Johns Hopkins University, Baltimore, MD. He has served as chief scientist at the Applied Physics Laboratory (APL) and faculty in the electrical and computer engineering department. He has been the chair of the IEEE Massive MIMO Standard Development Working Group. He is also the co-chair of the Massive MIMO Working Group. He has served as general co-chair of the IEEE Massive MIMO Workshop and session chair for many IEEE conferences. He previously worked for the System Architecture, Design, and Integration Directorate, a division of the Raytheon Company in Boston, MA. During his tenure at Raytheon, he developed system architecture, operational concepts, algorithms, and performance analyses for phased array radar systems. He also worked as a technical consultant for firms specializing in developing ASIC and FPGA for communication systems. His research interests are Multi-Channel System Architecture, Sensing, Next Generation Radar Systems, Wireless Communications 5G and Beyond, Artificial Intelligence, Quantum Information Science, Digital Signal Processing, and Biomedical Signal Processing. He is a fellow of the IEEE and a member of the IEEE Signal Processing Society, the IEEE Communications Society, and the IEEE Aerospace and Electronic Systems Society.
Agbotiname Lucky Imoize (Senior Member, IEEE) received the BEng degree (Hons.) in electrical and electronics engineering from Ambrose Alli University, Nigeria, in 2008 and the MSc degree in electrical and electronics engineering from the University of Lagos, Nigeria, in 2012. He is a lecturer in the Department of Electrical and Electronics Engineering at the University of Lagos, Nigeria. Before joining the University of Lagos, he was a lecturer at Bells University of Technology, Nigeria. He was, until recently, a researcher at the Ruhr University Bochum, Germany, under the sponsorship of the Nigerian petroleum technology development fund (PTDF) and the German academic exchange service (DAAD) through the Nigerian-German postgraduate program. He was awarded the Fulbright fellowship as a visiting research scholar at the Wireless@VT Laboratory, Bradley Department of Electrical and Computer Engineering, Virginia Tech., Blacksburg, USA, where he worked under the supervision of Prof. R. Michael Buehrer from 2017 to 2018. He worked as a core network products manager at ZTE, Nigeria, and as a Network Switching Subsystem Engineer at Globacom, Nigeria. His research interests cover the fields of 6G wireless communication, wireless security systems, and artificial intelligence. He is the vice chair of the IEEE Communication Society Nigeria chapter. He is a registered engineer with the Council for the Regulation of Engineering in Nigeria and a member of the Nigerian Society of Engineers.
List of Contributors
Taufik Abrão
Electrical Engineering Department
Londrina State University
Brazil
Abdullateef Ola Adebayo
Department of Electrical and Computer Engineering
Kwara State University
Malete
Nigeria
Anjana B. S.
Department of Communication Engineering, School of Electronics Engineering
Vellore Institute of Technology
Tamil Nadu, Vellore
India
Joseph Bamidele Awotunde
Department of Computer Science, Faculty of Information and Communication Sciences
University of Ilorin
Ilorin
Nigeria
Peace Oluwasijibomi Balogun
Department of Electrical and Computer Engineering
Kwara State University
Malete
Nigeria
Nivetha Baskar
Department of Communication Engineering, School of Electronics Engineering
Vellore Institute of Technology
Tamil Nadu, Vellore
India
Emil Björnson
Department of Computer Science
KTH Royal Institute of Technology
Stockholm
Sweden
Yashuai Cao
Department of Electronic Engineering
Tsinghua University
Beijing China
and
Beijing National Research Center for Information Science and Technology (BNRist)
Beijing
China
Daniel Castanheira
Instituto de Telecomunicações (IT), and Departamento de Electrónica
Telecomunicações e Informática (DETI)
Universidade de Aveiro
Aveiro
Portugal
Özlem Tuğfe Demir
Department of Electrical and Electronics Engineering
TOBB University of Economics and Technology
Ankara
Türkiye
Rui Dinis
Instituto de Telecomunicacões (IT), Faculdade de Ciências e Tecnologia
Universidade Nova de Lisboa
Lisboa
Portugal
Dipinkrishnan R
Department of Communication Engineering, School of Electronics Engineering
Vellore Institute of Technology
Tamil Nadu, Vellore
India
Unwana Macaulay Ekpe
Department of Electrical and Electronic Engineering, Faculty of Engineering
Akwa Ibom State University
Ikot Akpaden
Nigeria
Atílio Gameiro
Instituto de Telecomunicações (IT), and Departamento de Electrónica
Telecomunicações e Informática (DETI)
Universidade de Aveiro
Aveiro
Portugal
Vishnu Vardhan Gudla
Department of Electronics and Communication Engineering
Aditya Engineering College
Andhra Pradesh
Surampalem
India
David William Marques Guerra
Electrical Engineering Department
Londrina State University
Brazil
Ekram Hossain
Department of Electrical and Computer Engineering
University of Manitoba
Canada
Agbotiname Lucky Imoize
Department of Electrical and Electronics Engineering, Faculty of Engineering
University of Lagos
Akoka, Lagos
Nigeria
and
Department of Electrical Engineering and Information Technology
Ruhr University
Bochum
Germany
Segun Jacob
Department of Electrical and Computer Engineering
Kwara State University
Malete
Nigeria
Thiruvengadam Sundarrajan Jayaraman
Department of Electronics and Communication Engineering
Thiagarajar College of Engineering
Tamil Nadu, Madurai
India
Helen Sheeba John Kennedy
Department of Communication Engineering, School of Electronics Engineering
Vellore Institute of Technology
Tamil Nadu, Vellore
India
Wilson Souza Jr.
Electrical Engineering Department
Londrina State University
Brazil
Debdatta Kandar
Department of Information Technology
North Eastern Hill University
Shillong, Meghalaya
India
Joumana Kassam
Instituto de Telecomunicações (IT), and Departamento de Electrónica
Telecomunicações e Informática (DETI), Universidade de Aveiro
Aveiro
Portugal
Vinoth Babu Kumaravelu
Department of Communication Engineering, School of Electronics Engineering
Vellore Institute of Technology
Tamil Nadu, Vellore
India
Tiejun Lv
School of Information and Communication Engineering
Beijing University of Posts and Telecommunications (BUPT)
Beijing
China
José Carlos Marinello
Electrical Engineering Department
UTFPR
Brazil
Webert Montlouis
Applied Physics Laboratory (APL) and Electrical and Computer Engineering Department
Johns Hopkins University
Baltimore, MD
USA
Arthi Murugadass
School of Computer Science and
Engineering
Vellore Institute of Technology
Chennai, Tamil Nadu
India
Abdulwaheed Musa
Department of Electrical and Computer Engineering
Kwara State University
Malete
Nigeria
and
Centre for Artificial Intelligence and Machine Learning Systems
Kwara State University
Malete
Nigeria
and
Institute for Intelligent Systems
University of Johannesburg
Johannesburg
South Africa
Wei Ni
Commonwealth Scientific and Industrial Research Organisation
Sydney, NSW
Australia
Vetriveeran Rajamani
Department of Micro and Nano Electronics, School of Electronics Engineering
Vellore Institute of Technology
Tamil Nadu, Vellore
India
Rajeshkumar V
Department of Communication Engineering, School of Electronics Engineering
Vellore Institute of Technology
Tamil Nadu, Vellore
India
Parisa Ramezani
Department of Computer Science
KTH Royal Institute of Technology
Stockholm
Sweden
Poongundran Selvaprabhu
Department of Communication Engineering, School of Electronics Engineering
Vellore Institute of Technology
Tamil Nadu, Vellore
India
Adão Silva
Instituto de Telecomunicações (IT), and Departamento de Electrónica
Telecomunicações e Informática (DETI)
Universidade de Aveiro
Aveiro
Portugal
Arun Kumar Singh
Department of Electronics and Communication Engineering
Sikkim Manipal Institute of Technology
Sikkim Manipal University
Majitar, Sikkim
India
Velmurugan Periyakarupan Gurusamy Sivabalan
Department of Electronics and Communication Engineering
Thiagarajar College of Engineering
Tamil Nadu, Madurai
India
João Henrique Inacio de Souza
Electrical Engineering Department
Londrina State University
Brazil
Samarendra Nath Sur
Department of Electronics and Communication Engineering
Sikkim Manipal Institute of Technology
Sikkim Manipal University
Majitar, Sikkim
India
Anand Sreekantan Thampy
Centre for Nanotechnology Research
Vellore Institute of Technology
Tamil Nadu, Vellore
India
Hetong Wang
School of Information and Communication Engineering
Beijing University of Posts and Telecommunications (BUPT)
Beijing
China
Preface
The need to implement cutting-edge technologies to satisfy the demands of fast growing mobile usage by efficiently utilizing scarce spectrum resources is a critical requirement in modern wireless networks. Though several antenna configurations have been proposed to alleviate this problem, the existing techniques have not adequately addressed prevailing issues such as severe pathloss, limited network performance, interference, among others. A possible alternative is applying an advanced antenna technology called massive multiple-input multiple-output (MIMO). Massive MIMO is a key-enabling technology in ubiquitous 5G wireless systems, and it poses enormous prospects for the envisioned 6G and future wireless networks. Massive MIMO could integrate other cutting-edge technologies to improve rates, energy, and spectral efficiency, opening new frontiers in wireless communications.
In massive MIMO, a single base station (BS) can be exploited to eliminate inter-cell interference, employing directional beamforming. First, however, the problem of gaining an insightful understanding of the fundamental limits of massive MIMO persists. Second, the performance evaluation of massive MIMO under ideal and nonideal practical scenarios requires detailed investigation. Third, billions of massive devices communicate via open wireless channels, posing huge security risks to sensitive user data. Thus, robust security architectures for privacy preservation and security of critical information have become imperative in massive MIMO wireless communication networks.
This book presents applications and technology for massive MIMO in 6G and beyond. Specifically, the book presents a unified theoretical framework for analyzing the fundamental limits of massive MIMO, considering several practical network constraints. The book presents advanced signal processing algorithms to enable massive MIMO applications in realistic environments. Additionally, the book presents advanced mathematical tools to analyze multiuser dynamics in evolving wireless communication channels.
The key highlights of the book are outlined as follows:
The book provides industry and academic researchers with new insights into the real-world deployment scenarios, design and implementation, application, technological trends, and associated benefits of massive MIMO in emerging wireless communication systems.
The book addresses the need to design energy and spectral-efficient massive MIMO models to resolve several network issues, such as interference, pathloss, delay, traffic outage, and so on, in modern wireless communication systems.
The book critically examines the fundamental limits of massive MIMO and proffer solutions to revamp the traditional MIMO architecture toward addressing the vast network issues, especially at the wireless edge.
The book discusses critical security and privacy issues affecting all stakeholders in the wireless ecosystem, and provides practical and effective solutions to address these problems.
The book is structured into 13 chapters collected from industry experts and world-class academic researchers, resulting in diverse and high-quality work for the readers.
Chapter 1 provides introduction to massive MIMO for future wireless communication systems. Starting with the fundamentals of wireless communications and providing background information to facilitate understanding the book. The chapter examines how wireless communication has revolutionized the way we interact with technology, enabling seamless connectivity and communication between devices and networks without needing physical cables. As technology has advanced, each generation of wireless communication has brought significant improvements, leading us to the era of 5G and the promise of even more transformative advancements in the future with 6G and beyond. Wireless communication rely on electromagnetic waves to carry signals through the air or space. Understanding the fundamentals of wireless communication is essential to grasp how these systems work and their various applications. The key fundamentals are the electromagnetic spectrum, radio frequency, modulation, demodulation, antennas, propagation, signal-to-noise ratio (SNR), multiple access techniques, wireless standards, security, and latency, among others.
Chapter 2 examines the security and privacy of future wireless communication systems. The chapter summarizes the proliferating security vulnerability issues in wireless networks. Key challenges, including evolving security threats, massive MIMO security issues, and privacy risks from advanced data collection, are highlighted. Physical layer security methodologies, access control, monitoring tools, and privacy technologies are proposed as viable solutions. The impact of government policies, regulations, and emerging technologies like blockchain are highlighted. Last, open research issues are presented, emphasizing lightweight cryptography, new architectures, and aligning standards with the prevailing regulations.
Chapter 3 presents the applications of massive MIMO in wireless communication systems. The chapter presents a detailed capacity analysis, quantifying the immense throughput gains possible with large antenna arrays and wider bandwidths. Promising solutions drawing from existing literature are reviewed, including antenna architectures, estimation algorithms, signal processing advancements, network topologies, and cross-technology convergence that can help realize the potential of massive MIMO at mmWave/THz bands. The chapter identifies critical open-research challenges related to experimental validation, complexity, security, and standardization that need to be investigated to fully materialize the disruptive capabilities of massive MIMO for 6G and beyond. Last, the chapter provides a holistic perspective on harnessing the potential of massive MIMO in 6G by building on prior art while highlighting open problems, requiring further innovation tailored to wireless networks demands.
Chapter 4 considers cell-free massive MIMO technology and its applications in 6G. The chapter presents an overview of CF mMIMO systems, emphasizing the superiority of such systems over traditional cellular networks. The study covers the system architecture, namely centralized and distributed, along with recent research developments and beamforming algorithms to address the scalability issue of conventional CF systems. These techniques include the user-centric (UC) approach that seeks to build a realistic deployment, where each user equipment (UE) is only served by a small number of cooperative APs. In particular, the chapter considers the integration of CF with radio stripes (RSs) that improve robustness and reduce the high costs/complexity by employing a sequential topology, sharing the same cable for fronthaul and then connecting each RS to a single or multiple CPUs.
Chapter 5 focuses on localization in massive MIMO networks, considering the scenario from near-field to far-field. The chapter considers two subspace-based approaches; Multiple SIgnal Classification (MUSIC) and Estimation of Signal Parameters by Rotational Invariance Techniques (ESPRIT). Originally designed for far-field source localization, these methods have undergone several modifications to accommodate near-field scenarios. The chapter presents the foundations of MUSIC and ESPRIT algorithms and introduces some of their variations for both far-field and near-field localization by a single array of antennas. Finally, the chapter provides numerical examples to demonstrate the performance of the presented methods.
Chapter 6 considers energy-efficient (EE) uplink (UL) transmission in RIS-aided M-MIMO internet of things (IoT) systems. The chapter focuses on the EE UL transmission of M-MIMO IoT systems aided by an RIS. The work proposes and evaluates different schemes to minimize the total UL transmit power by optimizing the transmit power of IoT devices, the RIS phase-shift element, and the combining matrix at the BS. Particularly, special attention is given to manifold optimization techniques, which are well suited to the RIS phase-shifts optimization problems. Herein, the authors treat jointly via iterative alternating optimization (i-AO) approach, the three optimization variables: RIS phase-shift vector, BS combining matrix, and unit terminal (UT) power allocation vector. Extensive numerical results are provided and discussed, revealing that the proposed conjugate gradient (CG) method based on Riemannian manifold (RM) with the ZF combining achieves the highest power savings, being able to reduce the UL transmit power under typical operation conditions scenarios in comparison with conventional systems without RIS.
Chapter 7 deals with energy efficiency (EE) optimization of massive multiple-input multiple-output (M-MIMO) wireless communication systems aided by reconfigurable intelligent surfaces (RISs) and extralarge-scale M-MIMO (XL-MIMO) systems. The chapter presents a unified mathematical model for the wireless propagation channel and a set of optimization tools to optimize the EE in systems operating under realistic constraints. Particularly, two EE problem were formulated and solutions developed by considering different system configurations. In order to maximize the EE in RIS-aided M-MIMO systems, a sequential optimization method for joint RIS phase-shifts design, power allocation (PA), and optimization of the number of active BS antennas is proposed. Summarily, the chapter provides useful optimization tools and analytical frameworks, including techniques from convex optimization and evolutionary heuristic that can be applied to tackle a multitude of other EE-related problem formulations and feasible algorithmic solutions.
Chapter 8 presents NOMA-aided massive MIMO for next-generation networks. The system model of NOMA-aided mMIMO is presented and the sum rate expressions are derived for a two-user scenario. Results show that the proposed mMIMO-NOMA shows better performance than the existing mMIMO-OMA in terms of sum rate.
Chapter 9 proposes a low-complexity approach to achieving efficient hybrid precoding (HP) for millimeter-wave massive MIMO-NOMA systems. The chapter focuses on evaluating the performance of mmWave-mMIMO-NOMA systems in multiple user (MU) scenarios, with the overarching goal of designing a low-complexity HP scheme that elevates system performance. The chapter commences with an in-depth exploration of existing precoding algorithms pertinent to mMIMO-NOMA systems. Furthermore, the chapter proposes an innovative approach termed symmetric sequential over-relaxation (SSOR) complex regularized zero-forcing (CRZF) linear precoder. A pivotal aspect of the chapter involves comparing the performance of the proposed precoder against conventional linear precoders. The comparative analysis hones in on observable advantages such as improved spectral efficiency (SE), EE, and reduced computational complexity.
Chapter 10 investigates intelligent reflecting surfaces (IRS) and next-generation wireless systems. In particular, the chapter analyzes the slot-by-slot IRS reflection pattern design and two-timescale reflection pattern design schemes. For the slot-by-slot reflection optimization, an IRS to improve the propagation channel rank in mmWave massive MIMO systems without need to increase the transmit power budget was exploited. Then, the impact of the distributed IRS on the channel rank was analyzed. To further reduce the heavy overhead of channel training, channel state information (CSI) estimation, and feedback in time-varying MIMO channels, a two-timescale reflection optimization scheme is presented, where the IRS is configured relatively infrequently based on statistical CSI (S-CSI) and the active beamformers, and power allocation are updated based on quickly outdated instantaneous CSI (I-CSI) per slot. The achievable average sum-rate (AASR) of the system is maximized without excessive overhead of cascaded channel estimation. A recursive sampling particle swarm optimization (PSO) algorithm was developed to optimize the large-timescale IRS reflection pattern efficiently with reduced samplings of channel samples.
Chapter 11 conducts average bit error rate (ABER) performance evaluation of RIS-aided millimeter wave massive MIMO system under 3GPP 5G channels. The performance of the proposed system is evaluated in the indoor hotspot (InH)-indoor office and urban microcellular (UMi)-street canyon outdoor environments for 28 and 73 GHz mm Wave frequencies in 3GPP 5G channel models. The ABER of the proposed system is analyzed for indoor and outdoor scenarios by changing the number of reflector elements in RIS, and the number of BS antennas. The results showed that there is a significant SNR gain improvement while increasing the number of reflector elements and transmit antennas.
Chapter 12 explores massive MIMO for non-terrestrial wireless communication systems. The chapter offers a concise and comprehensive overview of the pivotal facets and formidable challenges inherent in non-terrestrial wireless communication networks, with a primary focus on signal processing for massive MIMO via satellites in the context of 5G and beyond. The themes presented reveal that orchestrating an intricate interplay among hundreds or even thousands of satellites and safeguarding a secure and dependable communications system will pose substantial challenges. Furthermore, and projecting forward, satellites are positioned as pivotal enablers for the forthcoming wave of wireless networks and will support a diverse array of applications, including massive machine-type communications, all underscored by the overarching need for secure connectivity. The chapter also presents ongoing issues related to spectrum allocation, interference management, and the need for security fortifications within the satellite communications sphere. As the demand for connectivity beyond the confines of earth continues to grow, the need for standardized and interoperable communication protocols becomes increasingly imperative. The chapter concludes by presenting recent solutions to problems in the ever-evolving landscape of non-terrestrial wireless communications and recommends a few important research pathways in this domain.
Chapter 13 integrates artificial intelligence and machine learning for channel estimation in massive MIMO wireless communication systems. Specifically, the chapter presents a comprehensive review of least squares (LS)- and minimum mean square error (MMSE)-based channel estimation in wireless communication systems, starting from single-input single-output (SISO) to massive MIMO in both flat and frequency-selective fading environments.
The book is an ideal reference to practitioners, industry and academic researchers, scientists, and engineers in the fields of wireless communications and networking, signal processing, 5G and 6G networks, massive MIMO standardization, antennas design, sensing and localization, channel modeling and measurement, artificial intelligence, machine learning, federated learning, terrestrial and non-terrestrial applications, security and privacy, and others. Also, the textbook is suitable for graduate and senior undergraduate courses in wireless communications and related fields.
The editors specially thank the reviewers of the original book proposal for their constructive suggestions. Special thanks to all authors of the chapters for their insightful contributions. Many thanks to the reviewers, and editorial assistants at Wiley and IEEE Press for their cooperation and support.
Agbotiname Lucky Imoize
Gelsenkirchen, North Rhine-Westphalia, Germany
Webert Montlouis
Baltimore, Maryland, United States
Acknowledgments
I extend my deepest gratitude to my family. Your unwavering support, patience, and love have been the foundation of all my endeavors, providing the strength and inspiration I needed to persevere. I am sincerely thankful to the editorial team at WILEY-IEEE Press for your exceptional leadership and guidance. Your expertise and dedication have been crucial in bringing this work to life. I am profoundly grateful to my friends for your persistent encouragement, moral support, and belief in my abilities. Your presence has been instrumental in my academic and professional growth. To my colleagues and students, thank you for continually inspiring me to explore new ideas and push the boundaries of knowledge. Your curiosity and enthusiasm have been a constant source of motivation and have enriched this journey immeasurably.
Webert MontlouisColumbia, Maryland, United States
I sincerely express my profound gratitude to God for His divine wisdom and faithfulness in editing this book. The book would not have been possible without the support of the Department of Electrical Engineering and Information Technology, Institute of Digital Communication, Ruhr University Bochum, Germany, and the University of Lagos, Nigeria. I wholeheartedly acknowledge the sponsorship from the Nigerian Petroleum Technology Development Fund (PTDF) and the German Academic Exchange Service (DAAD) through the Nigerian-German Postgraduate Program. Additionally, I am indebted to the Deeper Life Bible Church, Essen Region, North Rhine-Westphalia, Germany, for their unwavering support. Special thanks to my beloved wife, Kelly, and our sons, Lucius, Luke, Lucas, and Luther. Last, I sincerely thank the editorial team at WILEY-IEEE Press for their support.
Agbotiname Lucky ImoizeGelsenkirchen, North Rhine-Westphalia, Germany
1Fundamentals of Wireless Communications: Massive MIMO Essentials for 6G and Beyond
Webert Montlouis1 and Agbotiname Lucky Imoize2
1Johns Hopkins University, Baltimore, MD, USA
2Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos, Nigeria
1.1 Introduction
Wireless communication has revolutionized how we interact with technology, enabling seamless connectivity and communication between devices and networks without needing physical cables [1]. As technology has advanced, each generation of wireless communication has brought significant improvements, leading us to the era of 5G and the promise of even more transformative advancements in the future with 6G and beyond [2, 3]. Wireless communication relies on electromagnetic waves to carry signals through the air or space. Understanding the fundamentals of wireless communication is essential to grasp how these systems work and their various applications. The key fundamentals are the electromagnetic spectrum, radio frequency (RF), modulation, demodulation, antennas, propagation, signal-to-noise ratio (SNR), multiple access techniques, wireless standards, security, and latency.
The electromagnetic spectrum is the range of all possible frequencies of electromagnetic radiation. It includes radio waves, microwaves, infrared, visible light, ultraviolet, X-rays, and gamma rays. Different wireless communication technologies use different portions of this spectrum to transmit signals. In wireless communication, RF refers to the range of frequencies used to transmit and receive signals [4]. RF communication is most common and includes technologies like wireless fidelity (Wi-Fi), Bluetooth, cellular networks, and satellite communication.
Modulation is the process of impressing the information (voice, data, video, etc.) onto the carrier signal [5]. It allows the information to be transmitted efficiently over the wireless channel. Common modulation techniques include amplitude modulation (AM), frequency modulation (FM), and phase modulation (PM). Demodulation is the reverse modulation process, extracting information from the received modulated signal. Antennas are essential components in wireless communication systems. They transmit and receive electromagnetic waves, allowing devices to communicate with each other.
Antennas can be directional or omnidirectional, depending on their design and application. Propagation refers to the way electromagnetic waves travel through the environment. The wireless signal can propagate through free space, atmosphere, buildings, and other obstacles. Propagation can be affected by factors like path loss, fading, reflection, diffraction, and interference. SNR measures the strength of the desired signal compared to the background noise in the communication channel. A higher SNR indicates a better quality signal and improved communication reliability.
Multiple access techniques allow multiple devices to share the same communication channel efficiently. Common techniques include frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and orthogonal FDMA (OFDMA). Various organizations define wireless communication standards to ensure compatibility and efficient use of the wireless spectrum. For example, the Institute of Electrical and Electronics Engineers (IEEE) sets standards for Wi-Fi (e.g., 802.11ac and 802.11ax), and the Third-Generation Partnership Project (3GPP) defines cellular standards like 4G LTE and 5G [6].
Wireless communication is susceptible to security threats like eavesdropping, data interception, and unauthorized access. Encryption and other security protocols protect the data and ensure secure communication. Latency is the time delay between the transmission and reception of data. Low latency is crucial in applications requiring real-time responsiveness (e.g., online gaming and video conferencing). Understanding these fundamental concepts of wireless communication helps engineers, researchers, and industry professionals design and optimize wireless systems, develop new technologies, and create innovative applications that continue to shape the world of wireless connectivity.
1.1.1 A Brief History of Wireless Communication
A brief history describing the evolution of wireless communication, the next phase in wireless communication, the beyond 5G – the future of wireless communications, and the applications landscape is presented as follows.
Evolution of Wireless Communication
1G or First-generation mobile communication systems introduced in the 1980s, primarily based on analog technology.
2G or Second-generation mobile systems emerged in the early 1990s, utilizing digital technology to offer improved voice quality and the introduction of text messaging (SMS).
3G or Third-generation networks arrived in the early 2000s, providing faster data transfer, enabling mobile internet browsing, and supporting multimedia applications.
4G or Fourth-generation networks came into play around the mid-2000s, offering significantly faster data speeds, enhanced multimedia support, and the rise of app-based ecosystems.
The Next Phase in Wireless Communication
5G or Fifth-generation wireless technology began to be deployed in the late 2010s and early 2020s. It represents a significant leap in performance, promising ultra-fast data speeds, lower latency, increased capacity, and improved reliability. Some key features of 5G include:
Enhanced Mobile Broadband (eMBB): High-speed data transfer capable of delivering gigabit-per-second speeds, enabling seamless streaming, virtual reality (VR), and augmented reality (AR) experiences
[7]
.
Ultra-Reliable Low Latency Communications (URLLC): Reduced latency and increased reliability are critical for autonomous vehicles, remote surgeries, and industrial automation applications
[8]
.
Massive Machine-Type Communications (mMTC): 5G can connect a massive number of devices simultaneously, supporting the Internet of Things (IoT) and smart city applications [
9
,
10
].
Beyond 5G – The Future of Wireless Communications
As technology advances, researchers and industry experts are already exploring what comes after 5G. Several concepts and technologies are being considered for “Beyond 5G” or “6G”:
Terahertz (THz) Communication: Utilizing the higher-frequency spectrum (above 100 GHz) to achieve even faster data rates and greater capacity.
Integrated Satellite Communication: Integrating terrestrial and satellite networks to provide seamless global coverage.
Holographic Beamforming: Using advanced beamforming techniques to create highly focused, directional signals, improving efficiency and reducing interference.
Applications Landscape
5G and beyond have the potential to revolutionize various industries and daily life. Some of the potential applications include:
Enhanced virtual and augmented reality experiences.
Smart cities with connected infrastructure and IoT devices.
Advanced healthcare applications, such as remote surgeries and telemedicine.
Self-driving cars and intelligent transportation systems.
Improved real-time communication and collaboration tools.
Wireless communication has come a long way from its humble beginnings, and 5G represents a transformative step toward a more connected and efficient world. As technology continues to evolve, the possibilities of what future generations of wireless communication can achieve are boundless, promising a future of unprecedented connectivity and innovation.
1.1.2 Key Highlights of the Chapter
The following are the significant highlights of this chapter:
This chapter thoroughly explores the fundamental components of wireless communications.
This chapter is an invaluable resource for researchers who may not have a prior foundation in wireless communication systems, offering them a rapid pathway to acquaint themselves with the subject and empowering them to make substantial contributions to the evolution of communication systems.
Additionally, this chapter furnishes practical insights into the components of wireless communication and offers a rich set of references for those seeking to delve deeper into the subject.
1.1.3 Chapter Organization
The remaining part of this chapter is organized as follows. Section 1.2 covers digital and analog sources. Section 1.3 explores deterministic and random waveforms. Section 1.4 beams a laser focus on the propagation of electromagnetic waves. Section 1.5 examines the basics of information measures. Section 1.6 dissects the concept of wireless channel and information. Section 1.7 covers modulation and demodulation. Section 1.8 takes a closer look at the fundamentals of massive multiple input multiple output (MIMO) technology. Section 1.9 discusses critical security and privacy issues in emerging wireless communication systems. Finally, the conclusion to the chapter is drawn in Section 1.10.
1.2 Digital and Analog Sources
Digital and analog sources refer to different types of signals or data representations in electronic systems. These terms are commonly used in the context of various technologies, including signal processing, communication systems, and data storage. Digital signals are represented using discrete values, typically in the form of binary digits (bits). Each bit can take on one of the two values: 0 or 1. Digital sources have well-defined levels and are less susceptible to noise and interference. They are precise and can be easily manipulated using digital processing techniques. Digital sources include digital audio signals, binary data in computers, digital images, and any information that can be represented using a series of discrete values.
Analog signals are represented using continuous values that vary smoothly over time. These signals can take any value within a range. Analog sources can represent a wide range of values with infinite resolution. However, they are more susceptible to noise and degradation over long distances. Analog sources include traditional audio signals, analog sensors (e.g., temperature sensors), analog video signals, and any continuously varying signal.
In digital communication systems, the conversion of analog signals to digital data is a crucial step for efficient processing. This transformation is accomplished through analog-to-digital conversion (ADC), a process that translates analog signals into their digital counterparts. Applications of ADC are diverse, ranging from digitizing audio signals for recording purposes to converting analog sensor readings into digital data for seamless integration with computer systems [11]. Conversely, digital-to-analog conversion (DAC) plays a pivotal role in various applications, such as audio playback. DAC is the process of converting digital signals back into analog form, allowing for the recreation of the original analog signal. In the sphere of audio playback, this is exemplified when digital audio files are transformed into analog signals for playback through speakers or headphones, ensuring a high-fidelity and immersive auditory experience.
Some communication systems still use analog signals, especially in certain audio and video broadcasting applications. However, analog communication is more susceptible to noise. Many modern communication systems use digital signals for transmission due to their robustness against noise. Digital communication allows for error detection and correction. The choice between digital and analog depends on the specific requirements of a given application. Digital technologies have become dominant in many areas due to their precision, reliability, and ease of processing advantages.
1.3 Deterministic and Random Waveforms
Deterministic signals are characterized by their accurate definition through mathematical functions or equations, affording them a precise and predictable behavior at any given instant. These signals are entirely governed by the mathematical constructs that describe them, leaving no room for ambiguity and ensuring their complete determination [12]. For instance, a deterministic signal can be explained using a mathematical formula detailing its amplitude, frequency, and phase as functions of time or another independent variable. The inherent predictability of deterministic signals originates from their mathematical representations, empowering us to ascertain their values with certainty at any specific point in time. These signals often exhibit a rhythmic and repetitive nature, underscoring their predictability and structured patterns. Familiar instances of deterministic signals include sine waves, square waves, triangular waves, and any other signal that lends itself to precise description through a mathematical equation. In fields as diverse as signal processing, control systems, and communication systems, deterministic signals assume a critical role due to their predictability and the ease with which they can be subjected to rigorous mathematical analysis. Their well-defined nature facilitates comprehensive understanding and positions them as indispensable tools in advancing scientific and technological pursuits.
