Mobile networks that are capable of autonomously adapting itself to changes in their environment, while maintaining its desired objectives.
Published in Chapter:
The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks
Paulo Valente Klaine (University of Glasgow, UK), Oluwakayode Onireti (University of Glasgow, UK), Richard Demo Souza (Federal University of Santa Catarina (UFSC), Brazil), and Muhammad Ali Imran (University of Glasgow, UK)
Copyright: © 2019
|Pages: 23
DOI: 10.4018/978-1-5225-7458-3.ch001
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.