02763cam a22003618i 450000100090000000300040000900500170001300800410003001000170007102000300008802000260011804000230014404200080016705000250017508200290020008400230022924502240025225000190047626300090049526400840050430000130058833600260060133700280062733800270065550400510068250502330073352010850096665000360205165000220208765000680210970000310217777601930220822371394OSt20260113150704.0220104s2022    enk      b    001 0 eng    a  2021063108  a9781108832984q(hardback)  z9781108966559q(epub)  aDLCbengerdacDLC  apcc00aTK5103.2b.M315 202200a621.382223/eng/20220318  aTEC0670002bisacsh00aMachine Learning and Wireless Communications /cedited by Yonina C. Eldar, Weizmann Institute of Science, Andrea Goldsmith, Princeton University, Deniz Gündüz, Imperial Colleg, H. Vincent Poor, Princeton University.  aFirst edition.  a2206 1aCambridge, United Kingdom ;aNew York, NY :bCambridge University Press,c2022.  apages cm  atextbtxt2rdacontent  aunmediatedbn2rdamedia  avolumebnc2rdacarrier  aIncludes bibliographical references and index.2 aDeep 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.  a"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"--cProvided by publisher. 0aWireless communication systems. 0aMachine learning. 7aTECHNOLOGY & ENGINEERING / Signals & Signal Processing2bisacsh1 aEldar, Yonina C.,eeditor.08iOnline version:tMachine learning and wireless communications.bFirst edition.dCambridge, United Kingdom ; New York, NY : Cambridge University Press, 2022z9781108966559w(DLC) 2021063109