Machine Learning in Wireless Communication: A Survey

Machine Learning in Wireless Communication: A Survey

Neha Vaishnavi Sharma (Manipal University Jaipur, India) and Narendra Singh Yadav (Manipal University Jaipur, India)
DOI: 10.4018/978-1-5225-7458-3.ch007
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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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The purpose of this chapter is to provide the students, researchers, network engineers and a wide range of readers with a guide to the fundamental concepts, machine learning approaches and algorithms required for a basic understanding and implementation to the Wireless communications. This chapter is both a survey for existing works and a guide for researchers willing to apply Machine learning to problems in ad hoc networking.

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