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A01=Jun Zhang
A01=Yinan Zou
A01=Yong Zhou
A01=Youlong Wu
A01=Yuanming Shi
Age Group_Uncategorized
Age Group_Uncategorized
Author_Jun Zhang
Author_Yinan Zou
Author_Yong Zhou
Author_Youlong Wu
Author_Yuanming Shi
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Category1=Non-Fiction
Category=TJKT1
Category=TJKW
Category=UT
Category=UYQM
COP=United States
Delivery_Pre-order
Language_English
PA=Not yet available
Price_€100 and above
PS=Forthcoming
softlaunch

Machine Learning for Low-Latency Communications

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. See more
Current price €132.04
Original price €138.99
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A01=Jun ZhangA01=Yinan ZouA01=Yong ZhouA01=Youlong WuA01=Yuanming ShiAge Group_UncategorizedAuthor_Jun ZhangAuthor_Yinan ZouAuthor_Yong ZhouAuthor_Youlong WuAuthor_Yuanming Shiautomatic-updateCategory1=Non-FictionCategory=TJKT1Category=TJKWCategory=UTCategory=UYQMCOP=United StatesDelivery_Pre-orderLanguage_EnglishPA=Not yet availablePrice_€100 and abovePS=Forthcomingsoftlaunch

Will deliver when available. Publication date 01 Nov 2024

Product Details
  • Weight: 450g
  • Dimensions: 191 x 235mm
  • Publication Date: 01 Nov 2024
  • Publisher: Elsevier Science Publishing Co Inc
  • Publication City/Country: United States
  • Language: English
  • ISBN13: 9780443220739

About Jun ZhangYinan ZouYong ZhouYoulong WuYuanming Shi

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 masters 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. 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.

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