Resource Optimization in Wireless Communications

Fundamentals, Algorithms, and Applications

1st Edition - January 15, 2025
Authors: Lie-Liang Yang, Jia Shi, Kai-Ten Feng, Li-Hsiang Shen, Sau-Hsuan Wu, Ta-Sung Lee
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 3 0 0 9 2 - 9
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 3 0 0 9 3 - 6

Resource Optimization in Wireless Communications: Fundamentals, Algorithms, and Applications provides an easy-to-understand overview of the fundamentals of resource optimi… Read more

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Resource Optimization in Wireless Communications: Fundamentals, Algorithms, and Applications provides an easy-to-understand overview of the fundamentals of resource optimization, along with the latest algorithms and applications for emerging 5G, and beyond, wireless systems offering a variety of services. Additionally, it covers the principles and resource optimization of some systems expected in 6G.

This book is suitable for courses in wireless communications that cover the principles of multicarrier and OFDM, the theory of resource allocation, power allocation, and subcarrier allocation, as well as the principles and optimization of OTFS, ISAC, reflective intelligent surface (RIS)-assisted mmWave, and user-centric cell-free wireless systems. It is also an ideal self-study reference text for researchers and industry engineers who wish to deepen their knowledge while researching and developing wireless systems for 6G.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Preface
Acronyms
Chapter 1: Multicarrier communications and orthogonal frequency-division multiplexing
1.1. Introduction
1.2. Multicarrier transmission
1.2.1. Transmitter
1.2.2. Receiver
1.2.3. Implementation of multicarrier modulation and demodulation
1.3. Orthogonal frequency-division multiplexing
1.3.1. Principles of OFDM
1.3.2. Peak-to-average power ratio of OFDM signals
1.3.3. Frequency and time offset
1.4. Filtered OFDM
1.4.1. Single-numerology fOFDM
1.4.2. Multinumerology fOFDM
1.5. Modeling of wideband OFDM channels2
1.5.1. Correlation properties
1.5.2. Probability density function of fading envelope |hv|
1.6. Summary
Chapter 2: General theory and applications of resource optimization in wireless communications
2.1. Introduction
2.2. Optimization objectives
2.3. Optimization constraints
2.4. Classification of resource-allocation algorithms
2.5. Fairness and fairness-motivated resource allocation
2.5.1. Fairness measurement
2.5.2. Fairness-motivated resource allocation
2.5.3. Fairness with delay aware
2.6. Examples of applications
2.6.1. Single-hop links
2.6.2. Relay links
2.6.3. Multiple-input multiple-output (MIMO) links
2.6.4. Cellular wireless systems
2.6.5. Simultaneous wireless information and power transfer
2.7. Summary
Chapter 3: Power allocation
3.1. Introduction
3.2. Water-filling power allocation
3.3. Max-Min power allocation
3.4. Channel-inverse power allocation
3.5. Power allocation minimizing the sum of noise-to-signal ratio
3.6. Power allocation minimizing the p-norm of noise-to-signal ratio
3.7. Fairness-reliant power allocation
3.7.1. α-fair power allocation
3.7.2. Gα-fair power allocation
3.8. Power-allocation implementation for efficiency–fairness trade-off
3.9. Summary
Chapter 4: Subcarrier allocation
4.1. Introduction
4.2. Utility and cost matrices
4.3. Unfair greedy algorithm
4.4. Fair greedy algorithm
4.5. Maximal greedy algorithm
4.6. Worst user first algorithm
4.7. Worst channel-avoiding algorithm
4.8. Dynamic worst channel avoiding algorithm
4.9. Bidimensional worst channel avoiding algorithm
4.10. Iterative worst excluding algorithm
4.11. Best channel-seeking algorithm
4.12. Hungarian algorithm
4.13. Summary
Chapter 5: Beam and channel acquisitions in mmWave systems
5.1. Introduction
5.2. Hybrid beamforming in mmWave channels
5.2.1. Patch antenna pattern and radio-frequency array factor
5.2.2. Hybrid beamforming pattern
5.3. Beamformed MIMO channel model
5.3.1. Continuous-time channel impulse response
5.3.2. Discrete frequency and time CIRs with HBF
5.3.3. Modeling of frequency-domain spatial correlation
5.4. Beam and channel acquisition in HBF systems
5.4.1. Beam-space representation of HBF MIMO OFDM channels
5.4.2. Beam and channel training with compressive sensing
5.5. Summary
Chapter 6: Beam-based resource allocation in MIMO-OFDM networks
6.1. Introduction of MIMO and massive MIMO
6.1.1. MIMO systems
6.1.2. Massive MIMO
6.1.3. An example of a massive MIMO system
6.2. Beam selection and allocation in MIMO-OFDM systems
6.2.1. MIMO-OFDM systems
6.3. MIMO-OFDM networks with hybrid beamforming
6.3.1. Signal and channel models
6.3.2. Downlink-capacity evaluation
6.4. Beam allocations in single-BS HBF MIMO systems
6.5. Beam allocation in multi-BS HBF MIMO networks
6.5.1. Beam allocation with greedy approach
6.5.2. Beam allocation with particle swarm optimization
6.6. Beam allocations in CoMP HBF MIMO networks
6.6.1. System model
6.6.2. Signal model
6.6.3. UE capacity evaluation
6.6.4. Problem formulation and greedy algorithm
6.6.5. Simulation results
6.7. Summary
Chapter 7: Game-theory-assisted resource allocation
7.1. Introduction
7.1.1. Fundamental elements of a matching game
7.1.2. Fundamental elements of a coalitional game
7.2. Functional split networks
7.2.1. Problem description
7.2.2. Beam-allocation problem as a matching game
7.2.3. Beam-allocation problem as a coalitional game
7.3. In-band full-duplex networks
7.3.1. Problem description
7.3.2. Resource-block-allocation problem as a matching game
7.3.3. Resource-block-allocation problem as a coalitional game
7.4. Conclusion and open issues
Chapter 8: Machine/deep-learning-based resource allocation
8.1. Introduction
8.2. Deep-learning-based resource allocation
8.3. Deep learning in millimeter wave communications
8.3.1. Introduction to millimeter wave
8.3.2. Beamforming-training problem
8.4. Deep-neural-network-based resource allocation
8.4.1. Forward propagation for a neural network
8.4.2. Backward propagation for a neural network
8.4.3. Activation function
8.4.4. Batch normalization
8.5. Reinforcement-learning-based resource allocation
8.5.1. Q-learning-based resource allocation
8.5.2. Example of Q-learning
8.6. Deep-reinforcement-learning-based resource allocation
8.7. Conclusion and open issues
Chapter 9: Deep-reinforcement-learning-aided resource allocation in ultradense networks
9.1. Introduction
9.2. Modeling of a UDN
9.3. Resource-allocation problem formulation and analysis
9.4. DNN-based optimization
9.5. CIAQ algorithm
9.5.1. Design objectives of the CIAQ algorithm
9.5.2. Principles of the CIAQ algorithm
9.6. Performance results
9.7. Summary
Chapter 10: Resource allocation in multicell OFDMA systems
10.1. Introduction
10.2. Modeling of a multicell OFDMA system
10.3. Formulation of optimization problem for ICI-mitigation-oriented resource allocation
10.4. FIIDM and OOP algorithms
10.4.1. FIIDM algorithm
10.4.2. OOP algorithm
10.5. DDMC algorithm
10.6. CDMC algorithm
10.7. Extension
10.8. Performance results
10.9. Summary
Chapter 11: Distributed resource allocation in multicell MC/DS-CDMA systems
11.1. Introduction
11.2. Modeling of a multicell MC/DS-CDMA system
11.3. Problem formulation and analysis
11.4. Benchmark resource allocation with ICI mitigation
11.4.1. Benchmark of distributed subcarrier allocation (benchmark-SA)
11.4.2. Benchmark of distributed code allocation (benchmark-CA)
11.4.3. Benchmark intercell interference mitigation (benchmark-IM)
11.5. RAIM scheme
11.5.1. RAIM: subcarrier allocation (RAIM-SA)
11.5.2. RAIM: code allocation (RAIM-CA)
11.5.3. RAIM: ICI mitigation (RAIM-IM)
11.6. Characteristics and complexity analysis of RAIM
11.6.1. Characteristic analysis
11.6.2. Complexity analysis
11.7. Performance results
11.8. Summary
Chapter 12: Resource allocation for ultrareliable low-latency communications
12.1. Introduction
12.2. System model and problem formulation
12.2.1. Delay analysis
12.2.2. Problem formulation
12.3. The optimal FSM decision (OFD) scheme
12.3.1. Feasible set reduction (FSR)
12.3.2. Globally optimal solution search (GOSS)
12.4. Performance evaluation
12.5. Summary
Chapter 13: Resource allocation in massive machine-type communications
13.1. Introduction
13.2. Access control for mMTC
13.2.1. Access class barring
13.2.2. Group-based access control
13.2.3. Clustering algorithms
13.2.4. HetNet access control
13.3. Resource allocation in mMTC
13.3.1. Orthogonal and nonorthogonal resource allocation
13.3.2. Resource allocation for PRACH and PUSCH
13.3.3. Other advanced methods
13.4. Energy management in mMTC
13.4.1. General principles
13.4.2. Offloading mechanism
13.4.3. UAV-assisted networks
13.5. Advanced techniques in 5G NR
13.5.1. Power-saving techniques in 5G
13.5.2. Other techniques for power saving
13.6. Summary
Chapter 14: Resource optimization in reflective intelligent surface (RIS)-aided mmWave systems
14.1. Introduction
14.2. A conceptual example
14.3. RIS-mmWave: single-RIS single user
14.4. RIS-mmWave: single-RIS multiple users
14.5. RIS-mmWave: multiple-RISs multiple users
14.6. Concluding remarks
Chapter 15: Signaling and optimization in orthogonal time–frequency–space (OTFS) and related schemes
15.1. Introduction
15.2. Principles of OTFS
15.3. Relationship of OTFS with OSTF, OFDM, and SC-FDMA
15.4. Multiuser multiplexing and optimization in OTFS and OSTF systems
15.4.1. Uplink
15.4.2. Downlink
15.5. Concluding remarks
Chapter 16: Optimization in MIMO integrated sensing and communications (ISAC)
16.1. Introduction
16.2. Fundamentals of MIMO communications and MIMO sensing
16.2.1. MIMO communications
16.2.2. MIMO sensing
16.3. Resource optimization in MIMO ISAC systems
16.4. ISAC mmWave systems
16.5. Concluding remarks
Chapter 17: Optimization in user-centric cell-free (UCCF) wireless networks
17.1. Introduction
17.2. System models for UCCF wireless networks
17.3. Channel modeling and estimation
17.3.1. Channel modeling
17.3.2. Channel estimation
17.4. Access-point association of user equipments
17.5. Uplink detection and optimization
17.5.1. Uplink detection schemes
17.5.2. Uplink resource optimization
17.6. Downlink transmission and optimization
17.6.1. Downlink transmission with precoding
17.6.2. Downlink resource optimization
17.7. Concluding remarks
Bibliography
Index

Lie-Liang Yang

Lie-Liang Yang the professor of Wireless Communications in the School of Electronics and Computer Science at the University of Southampton, UK. He received his MEng and PhD degrees in communications and electronics from Northern (Beijing) Jiaotong University, Beijing, China in 1991 and 1997, respectively, and his BEng degree in communications engineering from Shanghai TieDao University, Shanghai, China in 1988. He has research interest in wireless communications, wireless networks and signal processing for wireless communications, as well as molecular communications and nano-networks. On these research topics, he has graduated 30+ PhD students and supervised 150+ master projects, published 400+ research papers in journals and conference proceedings, authored/co-authored three books and also published 10+ book chapters. He is a fellow of the IEEE, IET and of the AAIA, and was a distinguished lecturer of the IEEE VTS. He served as an associate editor to various journals and is currently a senior editor to the IEEE Access and a subject editor to the Electronics Letters. He was one of the guest editors for some special issues in, such as IEEE Journal on Selected Areas in Communications, IEEE Wireless Communication Magazine, IEEE Communication Magazine (2014), etc. He acted different roles, such as TPC/symposium/area/track/ chairs, for organization of conferences.

Affiliations and expertise

University of Southampton, UK

Jia Shi

Dr. Jia Shi received his MSc. and Ph.D degrees from the University of Southampton, UK, in 2010 and 2015, respectively. He was a research associate with Lancaster University, UK, during 2015-2017. Then, he worked as a research fellow with the 5GIC, University of Surrey, UK, from 2017 to 2018. Since 2018, he has been with Xidian University, China, and is now an Associate Professor in the National Key Lab. of Integrated Services Networks (ISN). His current research interests include resource allocation in wireless systems, covert communications, physical layer security, mmWave communications, satellite communications, etc. He is now serving as an Associate Editor to Electronic Letters, and an Editor to the International Journal of Communications System, and serves as a Guest Editor for China Communications.

Affiliations and expertise

Xidian University, China

Kai-Ten Feng

Kai-Ten Feng received the B.S. degree from the National Taiwan University, Taipei, Taiwan, in 1992, the M.S. degree from the University of Michigan, Ann Arbor, in 1996, and the Ph.D. degree from the University of California, Berkeley, in 2000.

Since August 2011, he has been a full Professor with the Department of Electronics and Electrical Engineering, National Chiao Tung University (NCTU) and National Yang Ming Chiao Tung University (NYCU), Hsinchu, Taiwan, where he was an Associate Professor and Assistant Professor from August 2007 to July 2011 and from February 2003 to July 2007, respectively. From July 2009 to March 2010, he was a Visiting Research Fellow with the Department of Electrical and Computer Engineering, University of California at Davis. Between 2000 and 2003, he was an In-Vehicle Development Manager/Senior Technologist with OnStar Corporation, a subsidiary of General Motors Corporation, where he worked on the design of future Telematics platforms and in-vehicle networks. His current research interests include AI-empowered broadband wireless networks, wireless indoor localization and tracking, and device-free wireless sensing technologies.

Dr. Feng received the Best Paper Award from the Spring 2006 IEEE Vehicular Technology Conference, which ranked his paper first among the 615 accepted papers. He also received the FutureTech Award in 2022 from National Science and Technology Council (NSTC), the Outstanding Youth Electrical Engineer Award in 2007 from the Chinese Institute of Electrical Engineering, and the Distinguished Researcher Award from NCTU in 2008, 2010, and 2011. He has also served on the technical program committees in various international conferences.

Affiliations and expertise

National Yang Ming Chiao Tung University

Li-Hsiang Shen

Li-Hsiang Shen received Ph.D. degree from the Institute of Communication Engineering, National Chiao Tung University (NCTU), Hsinchu, Taiwan, in 2020. From 2024, he will be an Assistant Professor with Department of Communication Engineering, National Central University (NCU), Taoyuan, Taiwan. From 2018 to 2019, he was a Visiting Scholar with the Next Generation Wireless Research Group of ECE, University of Southampton, U.K. From 2021 to 2023, he has been a Postdoc Researcher with Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University (NYCU), Hsinchu, Taiwan. From 2023, he was a Visiting Scholar with California PATH, Berkeley DeepDrive, University of California, Berkeley (UCB), USA. From 2018 to 2023, 40+ articles were published and he reviewed 100+ papers. He served as TPC Member in IEEE VTC2021-Fall, VTC2023-Fall, WPMC2023, and ICC2024. His research interests include wireless broadband, satellite communications, metasurfaces, wireless local area networks, integrated sensing and communications, wireless sensing, and machine/deep learning.

Affiliations and expertise

National Central University, Taiwan

Sau-Hsuan Wu

Sau-Hsuan Wu received the B.S. and the M.S. degrees from National Cheng Kung University, Tainan, Taiwan, in 1990 and 1993, respectively, both in engineering science, and the Ph.D. degree in electrical engineering from the University of Southern California (USC), Los Angeles, CA, USA, in 2003. From 1995 to 1999, and from 2004 to 2005, he served in the industry first as a senior circuit and system design engineer, and then as a technical consultant for wireless communication system designs. Since 2005, he has been with National Yang Ming Chiao Tung University, Hsinchu, Taiwan, and is currently a full Professor with the Institute of Communications Engineering, School of Electrical and Computer Engineering. His research interests lie in the areas of signal processing, system design and performance analysis for wireless communications systems and healthcare AIoT. He has won the best paper award in IEEE Greencom 2013, the first prize, and the second prize awards in 2018 and 2021 Mobileheroes Taiwan, respectively.

Affiliations and expertise

National Yang Ming Chiao Tung University

Ta-Sung Lee

Ta-Sung Lee received the B.S. degree from National Taiwan University in 1983, the M.S. degree from University of Wisconsin, Madison, in 1987, and the Ph.D. degree from Purdue University, W. Lafayette, IN, in 1989, all in electrical engineering. In 1990, he joined the Faculty of National Chiao Tung University (NCTU), Hsinchu, Taiwan, where he holds a position as Professor of Department of Electronics and Electrical Engineering. From 2005 to 2007, he was Chairman of Department of Communication Engineering, and from 2007 to 2008 and 2012 to 2014, he was Vice President for Student Affairs of NCTU. He was Vice President for Research and Development of NCTU from 2016 to 2021. He was Vice Chairman and Chairman of IEEE Communications Society Taipei Chapter from 2005 to 2008, an Associate Editor of IEEE Transactions on Signal Processing from 2009 to 2013, and IEEE Signal Processing Society Regional Director-at-Large for Region 10 from 2009 to 2013. Dr. Lee was appointed Commissioner of National Communications Commission (NCC) by the Premier of Taiwan, for the term 2008-2010. He was Chairman of Telecom Technology Center, a government funded agency for telecommunications R&D, from 2013 to 2016. He has been Director of IoT & Intelligent Systems Research Center of NCTU since 2017, and Senior Vice President of National Yang Ming Chiao Tung University (NYCU, a merger of National Yang-Ming University and NCTU) since 2021. Dr. Lee is active in research and development in advanced techniques for wireless communications, such as MIMO systems, mobile network resource management, and advanced radar systems for autonomous vehicles. He has led many collaborative projects in several national research programs, such as “Program for Promoting Academic Excellence of Universities– Phases I and II,” “National Science and Technology Program for Telecommunications” and “4G Mobile Communications Research Program,” “B5G/6G Wireless Communications and Networking Technologies Program,” and “Featured Areas Research Center Program of the Higher Education Sprout Project.” Dr. Lee has won several awards for his research, engineering and education contributions; these include National Science Council (NSC) Excellent Research Award, Young Electrical Engineer Award of the Chinese Institute of Electrical Engineering (CIEE), Distinguished Electrical Engineering Professor Award of CIEE, NCTU Distinguished Scholar Award, and NCTU Teaching Award. He is an IEEE Fellow, IET Fellow, AAIA Fellow and Advance HE Principal Fellow.

Affiliations and expertise

National Yang Ming Chiao Tung University, Taiwan

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Resource Optimization in Wireless Communications

Fundamentals, Algorithms, and Applications

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Institutional subscription on ScienceDirect

Resources

Lie-Liang Yang

Jia Shi

Kai-Ten Feng

Li-Hsiang Shen

Sau-Hsuan Wu

Ta-Sung Lee