How can machine learning help the design of future communication networks – and how can future networks meet the demands of emerging machine learning applications? Discover the interactions between two of the most transformative and impactful technologies of our age in this comprehensive book. First, learn how modern machine learning techniques, such as deep neural networks, can transform how we design and optimize future communication networks. Accessible introductions to concepts and tools are accompanied by numerous real-world examples, showing you how these techniques can be used to tackle longstanding problems. Next, explore the design of wireless networks as platforms for machine learning applications – an overview of modern machine learning techniques and communication protocols will help you to understand the challenges, while new methods and design approaches will be presented to handle wireless channel impairments such as noise and interference, to meet the demands of emerging machine learning applications at the wireless edge.
Edited by:
Yonina C. Eldar (Weizmann Institute of Science Israel),
Andrea Goldsmith (Princeton University,
New Jersey),
Deniz Gündüz (Imperial College of Science,
Technology and Medicine,
London),
H. Vincent Poor (Princeton University,
New Jersey)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Edition: New edition
Dimensions:
Height: 251mm,
Width: 177mm,
Spine: 29mm
Weight: 1.200kg
ISBN: 9781108832984
ISBN 10: 1108832989
Pages: 554
Publication Date: 04 August 2022
Audience:
College/higher education
,
Primary
Format: Hardback
Publisher's Status: Active
Preface; 1. Machine learning and communications: an introduction Deniz Gündüz, Yonina Eldar, Andrea Goldsmith and H. Vincent Poor; Part I. Machine Learning for Wireless Networks: 2. Deep neural networks for joint source-channel coding David Burth Kurka, Milind Rao, Nariman Farsad, Deniz Gündüz and Andrea Goldsmith; 3. Neural network coding Litian Liu, Amit Solomon, Salman Salamatian, Derya Malak and Muriel Medard; 4. Channel coding via machine learning Hyeji Kim; 5. Channel estimation, feedback and signal detection Hengtao He, Hao Ye, Shi Jin and Geoffrey Y. Li; 6. Model-based machine learning for communications Nir Shlezinger, Nariman Farsad, Yonina Eldar and Andrea Goldsmith; 7. Constrained unsupervised learning for wireless network optimization Hoon Lee, Sang Hyun Lee and Tony Q. S. Quek; 8. Radio resource allocation in smart radio environments Alessio Zappone and Mérouane Debbah; 9. Reinforcement learning for physical layer communications Philippe Mary, Christophe Moy and Visa Koivunen; 10. Data-driven wireless networks: scalability and uncertainty Feng Yin, Yue Xu and Shuguang Cui; 11. Capacity estimation using machine learning Ziv Aharoni, Dor Zur, Ziv Goldfeld and Haim Permuter; Part II. Wireless Networks for Machine Learning: 12. Collaborative learning on wireless networks: an introductory overview Mehmet Emre Ozfatura, Deniz Gündüz and H. Vincent Poor; 13. Optimized federated learning in wireless networks with constrained resources Shiqiang Wang, Tiffany Tuor and Kin K. Leung; 14. Quantized federated learning Nir Shlezinger, Mingzhe Chen, Yonina Eldar, H. Vincent Poor and Shuguang Cui; 15. Over-the-air computation for distributed learning over wireless networks Mohammad Mohammadi Amiri and Deniz Gündüz; 16. Federated knowledge distillation Hyowoon Seo, Seungeun Oh, Jihong Park, Seong-Lyun Kim and Mehdi Bennis; 17. Differentially private wireless federated learning Dongzhu Liu, Amir Sonee, Stefano Rini and Osvaldo Simeone; 18. Timely wireless edge inference Sheng Zhou, Wenqi Shi, Xiufeng Huang and Zhisheng Niu.
Yonina C. Eldar is a professor of Electrical Engineering at the Weizmann Institute of Science, where she heads the Center for Biomedical Engineering and Signal Processing. She is also a visiting professor at MIT and at the Broad Institute, and an adjunct professor at Duke University. She is a member of the Israel Academy of Sciences and Humanities, an IEEE fellow, and a EURASIP fellow. Andrea Goldsmith is the Dean of Engineering and Applied Science and the Arthur LeGrand Doty Professor of Electrical Engineering at Princeton University. She is a member of the US National Academy of Engineering and the American Academy of Arts and Sciences. In 2020, she received the Marconi Prize. Deniz Gündüz is a professor of Information Processing in the Electrical and Electronic Engineering Department of Imperial College London in the UK, where he serves as the Deputy Head of the Intelligent Systems and Networks Group. He is also a part-time faculty member at the University of Modena and Reggio Emilia in Italy. H. Vincent Poor is the Michael Henry Strater University Professor at Princeton University. He is a member of the US National Academy of Engineering and the US National Academy of Sciences. In 2017, he received the IEEE Alexander Graham Bell Medal.
Reviews for Machine Learning and Wireless Communications
'Recommended.' J. Brzezinski, Choice
