Overview of Machine Learning Approaches for Wireless Communication

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Author(s): Tolga Ensari (Istanbul University, Turkey), Melike Günay (Istanbul Kultur University, Turkey), Yağız Nalçakan (Altinbas University, Turkey)and Eyyüp Yildiz (Erzincan University, Turkey)
Copyright: 2019
Pages: 18
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.ch006

Keywords: Information Science Reference / Media & Communications / Networking and Telecommunications / Wireless Systems

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Abstract

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is continuously changing. Recent developments in smart phones and other devices like drones, wearable devices, machines with sensors need reliable communication within internet of things (IoT) systems. For this purpose, artificial intelligence can increase the security and reliability and manage the data that is generated by the wireless systems. In this chapter, the authors investigate several machine learning techniques for wireless communications including deep learning, which represents a branch of artificial neural networks.

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Overview of Machine Learning Approaches for Wireless Communication

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