Machine Learning And Wireless Communications
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Deep Learning in Wireless Communications
The book offers a focused examination of deep learning-based wireless communication systems and their applications. While both principles and engineering practice are explored, greater emphasis is placed on the latter. The book offers an in-depth exploration of major topics such as cognitive spectrum intelligence, learning resource allocation optimization, transmission intelligence, learning traffic and mobility prediction, and security in wireless communication. Notably, the book provides a comprehensive and systematic treatment of practical issues related to intelligent wireless communication, making it particularly useful for those seeking to learn about practical solutions in AI-based wireless resource management. This book is a valuable resource for researchers, engineers, and graduate students in the fields of wireless communication, telecommunications, and related areas.
Applications of Machine Learning in Wireless Communications
Author: Ruisi He
language: en
Publisher: Institution of Engineering and Technology
Release Date: 2019-06-20
Machine learning explores the study and development of algorithms that can learn from and make predictions and decisions based on data. Applications of machine learning in wireless communications have been receiving a lot of attention, especially in the era of big data and IoT, where data mining and data analysis technologies are effective approaches to solving wireless system evaluation and design issues.
Machine Learning for Wireless Communications and Networking
Machine Learning for Wireless Communications and Networking: An Introduction gives an easy-to-understand introduction to machine learning methods and techniques and their application to wireless communications. The book covers a wide range of machine learning techniques, starting with concepts related to statistical signal processing (i.e. decision/detection and estimation), taking advantage of the commonality of knowledge between statistical learning and statistical communication theory that the electronic engineer will be familiar with. Each chapter focuses on a class of machine learning techniques, clearly explaining the principles with a supporting range of examples in general wireless communications, wireless networks, sensor networks, and signal processing. Every chapter also has a dedicated section applying machine learning techniques to specific, state-of-the-art wireless network applications. Machine Learning for Wireless Communications and Networking: An Introduction is ideal for graduate and senior undergraduate students in wireless communications and networking who need to understand and apply machine learning techniques, researchers in wireless communications, signal processing, and wireless networks who need background knowledge in machine learning for wireless systems and networks, and engineers and professionals in the wireless communications and networking industry seeking to learn this important new technology which is having a major impact in the field.