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Machine Learning for Low-Latency Communications
- 1st Edition - November 1, 2024
- Authors: Yong Zhou, Yinan Zou, Youlong Wu, Yuanming Shi, Jun Zhang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 2 0 7 3 - 9
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 2 0 7 4 - 6
Machine Learning for Low-Latency Communications presents the principles and practice of various deep learning methodologies for mitigating three critical latency component… Read more
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Request a sales quoteMachine Learning for Low-Latency Communications presents the principles and practice of various deep learning methodologies for mitigating three critical latency components: access latency, transmission latency, and processing latency. In particular, the book develops learning to estimate methods via algorithm unrolling and multiarmed bandit for reducing access latency by enlarging the number of concurrent transmissions with the same pilot length. Task-oriented learning to compress methods based on information bottleneck are given to reduce the transmission latency via avoiding unnecessary data transmission.
Lastly, three learning to optimize methods for processing latency reduction are given which leverage graph neural networks, multi-agent reinforcement learning, and domain knowledge. Low-latency communications attracts considerable attention from both academia and industry, given its potential to support various emerging applications such as industry automation, autonomous vehicles, augmented reality and telesurgery. Despite the great promise, achieving low-latency communications is critically challenging. Supporting massive connectivity incurs long access latency, while transmitting high-volume data leads to substantial transmission latency.
- Presents the challenges and opportunities of leveraging data and model-driven machine learning methodologies for achieving low-latency communications
- Explains the principles and practices of modern machine learning algorithms (e.g., algorithm unrolling, multiarmed bandit, graph neural network, and multi-agent reinforcement learning) for achieving low-latency communications
- Gives design, modeling, and optimization methods for low-latency communications that apply appropriate learning methods to solve longstanding problems
- Provides full details of the simulation setup and benchmarking algorithms, with downloadable code
- Outlines future research challenges and directions
Academic researchers, professional engineers, post graduate students in communications engineering
Part 1: Introduction and Overview
1. Introduction and overview
Part 2: Learning to Estimate for Access Latency Reduction
2. Learning to estimate via group-sparse based algorithm unrolling
3. Learning to estimate via proximal gradient-based algorithm unrolling
4. Learning to detect via multiarmed bandit (MAB)
Part 3: Learning to Compress for Transmission Latency Reduction
5. Learning to compress via information bottleneck
6. Learning to compress via robust information bottleneck with digital modulation
7. Learning to compress for multi-device cooperative edge inference
Part 4: Learning to Optimize for Processing Latency Reduction
8. Learning to optimize via graph neural networks
9. Learning to optimize via knowledge guidance
10. Learning to optimize via decentralized multi-agent reinforcement learning
Part 5: Conclusions
11. Conclusions and Future Research Directions
1. Introduction and overview
Part 2: Learning to Estimate for Access Latency Reduction
2. Learning to estimate via group-sparse based algorithm unrolling
3. Learning to estimate via proximal gradient-based algorithm unrolling
4. Learning to detect via multiarmed bandit (MAB)
Part 3: Learning to Compress for Transmission Latency Reduction
5. Learning to compress via information bottleneck
6. Learning to compress via robust information bottleneck with digital modulation
7. Learning to compress for multi-device cooperative edge inference
Part 4: Learning to Optimize for Processing Latency Reduction
8. Learning to optimize via graph neural networks
9. Learning to optimize via knowledge guidance
10. Learning to optimize via decentralized multi-agent reinforcement learning
Part 5: Conclusions
11. Conclusions and Future Research Directions
- No. of pages: 366
- Language: English
- Edition: 1
- Published: November 1, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780443220739
- eBook ISBN: 9780443220746
YZ
Yong Zhou
Yong Zhou received the B.Sc. and M.Eng. degrees from Shandong University, Jinan, China, in 2008 and 2011, respectively, and the Ph.D. degree from the University of Waterloo, Waterloo, ON, Canada, in 2015. From Nov. 2015 to Jan. 2018, he worked as a postdoctoral research fellow in the Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada. He is currently an Assistant Professor in the School of Information Science and Technology, ShanghaiTech University, Shanghai, China. He was the track co-chair of IEEE VTC 2020 Fall and 2023 Spring, as well as the general co-chair of IEEE ICC 2022 workshop on edge artificial intelligence for 6G. He co-authored the book Mobile Edge Artificial Intelligence: Opportunities and Challenges (Elsevier 2021). His research interests include 6G communications, edge intelligence, and Internet of Things.
Affiliations and expertise
Shandong University, Jinan, ChinaYZ
Yinan Zou
Yinan Zou received the B.E. degree in electronic information engineering from Chongqing University, Chongqing, China, in 2020. He is currently pursuing the master’s degree with the School of Information Science and Technology, ShanghaiTech University, Shanghai, China. His research interests include learning to optimize, compressed sensing, and federated learning.
Affiliations and expertise
ShanghaiTech University, China.YW
Youlong Wu
Youlong Wu obtained his B.S. degree in electrical engineering from Wuhan University, Wuhan, China, in 2007. He received the M.S. degree in electrical engineering from Shanghai Jiaotong University, Shanghai, China, in 2011. In 2014, he received the Ph.D. degree at Telecom ParisTech, in Paris, France. In December 2014, he worked as a postdoc at the Institute for Communication Engineering, Technical University Munich (TUM), Munich, Germany. In 2017, he joined the School of Information Science and Technology at ShanghaiTech University. He obtained the TUM Fellowship in 2014 and is an Alexander von Humboldt research fellow. His research interests in Communication Theory, Information Theory and its applications e.g., coded caching, distributed computation, and machine learning.
Affiliations and expertise
ShanghaiTech University, ChinaYS
Yuanming Shi
Yuanming Shi received the B.S. degree in electronic engineering from Tsinghua University, Beijing, China, in 2011. He received the Ph.D. degree in electronic and computer engineering from The Hong Kong University of Science and Technology (HKUST), in 2015. Since September 2015, he has been with the School of Information Science and Technology in ShanghaiTech University, where he is currently a tenured Associate Professor. He visited University of California, Berkeley, CA, USA, from October 2016 to February 2017. His research areas include optimization, machine learning, wireless communications, and their applications to 6G, IoT, and edge AI. He was a recipient of the 2016 IEEE Marconi Prize Paper Award in Wireless Communications, the 2016 Young Author Best Paper Award by the IEEE Signal Processing Society, and the 2021 IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award. He is also an editor of IEEE Transactions on Wireless Communications, IEEE Journal on Selected Areas in Communications, and Journal of Communications and Information Networks.
Affiliations and expertise
ShanghaiTech University, ChinaJZ
Jun Zhang
Jun Zhang received the B.Eng. degree in Electronic Engineering from the University of Science and Technology of China in 2004, the M.Phil. degree in Information Engineering from the Chinese University of Hong Kong in 2006, and the Ph.D. degree in Electrical and Computer Engineering from the University of Texas at Austin in 2009. He is an Associate Professor in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology. His research interests include wireless communications and networking, mobile edge computing and edge AI, and cooperative AI.
Dr. Zhang co-authored the book Fundamentals of LTE (Prentice-Hall, 2010). He is a co-recipient of several best paper awards, including the 2021 Best Survey Paper Award of the IEEE Communications Society, the 2019 IEEE Communications Society & Information Theory Society Joint Paper Award, and the 2016 Marconi Prize Paper Award in Wireless Communications. Two papers he co-authored received the Young Author Best Paper Award of the IEEE Signal Processing Society in 2016 and 2018, respectively. He also received the 2016 IEEE ComSoc Asia-Pacific Best Young Researcher Award. He is an Editor of IEEE Transactions on Communications, and was an editor of IEEE Transactions on Wireless Communications (2015-2020). He served as a MAC track co-chair for IEEE Wireless Communications and Networking Conference (WCNC) 2011 and a co-chair for the Wireless Communications Symposium of IEEE International Conference on Communications (ICC) 2021. He is an IEEE Fellow and an IEEE ComSoc Distinguished Lecturer.
Affiliations and expertise
The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong