9781108832984-1108832989-Machine Learning and Wireless Communications

Machine Learning and Wireless Communications

ISBN-13: 9781108832984
ISBN-10: 1108832989
Edition: New
Author: H. Vincent Poor, Andrea Goldsmith, Yonina C. Eldar, Deniz Gündüz
Publication date: 2022
Publisher: Cambridge University Press
Format: Hardcover 554 pages
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Book details

ISBN-13: 9781108832984
ISBN-10: 1108832989
Edition: New
Author: H. Vincent Poor, Andrea Goldsmith, Yonina C. Eldar, Deniz Gündüz
Publication date: 2022
Publisher: Cambridge University Press
Format: Hardcover 554 pages

Summary

Machine Learning and Wireless Communications (ISBN-13: 9781108832984 and ISBN-10: 1108832989), written by authors H. Vincent Poor, Andrea Goldsmith, Yonina C. Eldar, Deniz Gündüz, was published by Cambridge University Press in 2022. With an overall rating of 4.0 stars, it's a notable title among other Electrical & Electronics (Engineering) books. You can easily purchase or rent Machine Learning and Wireless Communications (Hardcover) from BooksRun, along with many other new and used Electrical & Electronics books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $13.34.

Description

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.

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