Machine Learning for Low-Latency Communications
Published on 10. October 2024
365 pages
978-0-443-22074-6 (ISBN)
Description
Machine 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
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Yong Zhou | Yinan Zou | Youlong Wu
Machine Learning for Low-Latency Communications
Book
10/2024
Academic Press
€143.50
Shipment within 15-20 days
Persons
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.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.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.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.
Content
Part 1: Introduction and Overview1. Introduction and overviewPart 2: Learning to Estimate for Access Latency Reduction2. Learning to estimate via group-sparse based algorithm unrolling3. Learning to estimate via proximal gradient-based algorithm unrolling4. Learning to detect via multiarmed bandit (MAB)Part 3: Learning to Compress for Transmission Latency Reduction 5. Learning to compress via information bottleneck6. Learning to compress via robust information bottleneck with digital modulation7. Learning to compress for multi-device cooperative edge inferencePart 4: Learning to Optimize for Processing Latency Reduction 8. Learning to optimize via graph neural networks9. Learning to optimize via knowledge guidance10. Learning to optimize via decentralized multi-agent reinforcement learningPart 5: Conclusions 11. Conclusions and Future Research Directions
