A survey of machine learning techniques applied to self organizing cellular networks

Valente Klaine, P., Imran, M. A. , Onireti, O. and Souza, R. D. (2017) A survey of machine learning techniques applied to self organizing cellular networks. IEEE Communications Surveys and Tutorials, 19(4), pp. 2392-2431. (doi:10.1109/COMST.2017.2727878)

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Abstract

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

Item Type:Articles
Additional Information:The authors would like to acknowledge the support from the DARE project grant (No. EP/P028764/1) under the EPSRC’s Global Challenges Research Fund (GCRF) allocation.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Valente Klaine, Paulo and Imran, Professor Muhammad and Onireti, Oluwakayode
Authors: Valente Klaine, P., Imran, M. A., Onireti, O., and Souza, R. D.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Communications Surveys and Tutorials
Publisher:IEEE
ISSN:1553-877X
ISSN (Online):1553-877X
Published Online:17 July 2017
Copyright Holders:Copyright © 2017 IEEE
First Published:First published in IEEE Communications Surveys and Tutorials 19(4): 2392-2431
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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