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Machine Learning in Wireless Communication: A Survey

Machine Learning in Wireless Communication: A Survey
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Author(s): Neha Vaishnavi Sharma (Manipal University Jaipur, India)and Narendra Singh Yadav (Manipal University Jaipur, India)
Copyright: 2019
Pages: 21
Source title: Next-Generation Wireless Networks Meet Advanced Machine Learning Applications
Source Author(s)/Editor(s): Ioan-Sorin Comşa (Brunel University London, UK)and Ramona Trestian (Middlesex University, UK)
DOI: 10.4018/978-1-5225-7458-3.ch007

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Abstract

As the circumstances are changing, mankind has turned out to be more inclined to snappy and speedier correspondence and access to information. The correspondence happens in numerous structures (e.g., presently, this correspondence is all the more a virtual substance than a physical one). So as to keep up fast correspondence, the coming age will depend on exceptionally tried and true, canny and self-learning/self-modifying correspondence organizers. In this context, this chapter reviews the most important machine learning techniques with the direct applicability in wireless ad-hoc systems. A guide of machine learning methods and their relevance is also provided. Different applications of ad-hoc wireless networks are discussed in terms of energy-aware communications, optimal node deployment and localization, resource allocation, and scheduling.

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