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The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks

The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks

Paulo Valente Klaine, Oluwakayode Onireti, Richard Demo Souza, Muhammad Ali Imran
Copyright: © 2019 |Pages: 23
ISBN13: 9781522574583|ISBN10: 1522574581|EISBN13: 9781522574590
DOI: 10.4018/978-1-5225-7458-3.ch001
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MLA

Klaine, Paulo Valente, et al. "The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks." Next-Generation Wireless Networks Meet Advanced Machine Learning Applications, edited by Ioan-Sorin Comşa and Ramona Trestian, IGI Global, 2019, pp. 1-23. https://doi.org/10.4018/978-1-5225-7458-3.ch001

APA

Klaine, P. V., Onireti, O., Souza, R. D., & Imran, M. A. (2019). The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks. In I. Comşa & R. Trestian (Eds.), Next-Generation Wireless Networks Meet Advanced Machine Learning Applications (pp. 1-23). IGI Global. https://doi.org/10.4018/978-1-5225-7458-3.ch001

Chicago

Klaine, Paulo Valente, et al. "The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks." In Next-Generation Wireless Networks Meet Advanced Machine Learning Applications, edited by Ioan-Sorin Comşa and Ramona Trestian, 1-23. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7458-3.ch001

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Abstract

In this chapter, a brief overview of the role and applications of machine learning (ML) algorithms in future wireless cellular networks is presented, more specifically, in the context of self-organizing networks (SONs). SON is a promising and innovative concept, in which future networks are expected to analyze and use historical data in order to improve and adapt themselves to the network conditions. For this to be possible, however, algorithms that are capable of extracting patterns from data and learn from previous actions are necessary. This chapter highlights the utilization and possible applications of ML algorithms in future cellular networks. A brief introduction of ML and SON is presented, followed by an analysis of current state of the art solutions involving ML in SON. Lastly, guidelines on the utilization of intelligent algorithms in SON and future research trends in the area are highlighted and conclusions are drawn.

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