Machine Learning and Wireless Communications

Author:

Eldar Yonina C.,Goldsmith Andrea,Gündüz Deniz,Poor H. Vincent

Abstract

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.

Publisher

Cambridge University Press

Cited by 18 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. On differential privacy for federated learning in wireless systems with multiple base stations;IET Communications;2024-01-17

2. Data Augmentation for Deep Receivers;IEEE Transactions on Wireless Communications;2023-11

3. Machine Learning Based Node Selection for UWB Network Localization;MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM);2023-10-30

4. Online Meta-Learning for Hybrid Model-Based Deep Receivers;IEEE Transactions on Wireless Communications;2023-10

5. Decomposing the Training of Deep Learned Turbo codes via a Feasible MAP Decoder;2023 12th International Symposium on Topics in Coding (ISTC);2023-09-04

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