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. Provides easy comprehension and rapid application of machine learning knowledge for the reader with a background in communications, networking, and signal processing Comprehensive and easy-to-understand presentation of machine learning techniques, supported with a range of examples A detailed presentation of Statistical Inference as a foundation for machine learning, while also providing a foundation in general for communications engineering, signal processing, control and automation, cyber-physical systems, Internet of Things, and cybersecurity Presents statistical signal processing principles that are commonly used in communications engineering, signal processing, stochastic control, and networking Presents state of the art applications: signal detection and interception, spectrum sensing, radio access networks, radio resource management, cognitive radio, beamforming in MIMO communications, amongst many others Includes computer programming exercises, as well as exercises showing the use of software tools, facilitating interactive learning
