Machine learning and wireless communications /
edited by Yonina C. Eldar, Weizmann Institute of Science, Andrea Goldsmith, Princeton University, Deniz Gündüz, Imperial Colleg, H. Vincent Poor, Princeton University.
- 1 online resource (xiv, 544 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 20 Jun 2022).
Deep neural networks for joint source-channel coding / David Burth Kurka, Milind Rao, Nariman Farsad, Deniz Gündüz, Andrea Goldsmith -- Timely wireless edge inference / Sheng Zhou, Wenqi Shi, Xiufeng Huang, and Zhisheng Niu.
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.