Cell fault management using machine learning techniques

Mulvey, D., Foh, C. H., Imran, M. A. and Tafazolli, R. (2019) Cell fault management using machine learning techniques. IEEE Access, 7, pp. 124514-124539. (doi:10.1109/ACCESS.2019.2938410)

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Abstract

This paper surveys the literature relating to the application of machine learning to fault management in cellular networks from an operational perspective. We summarise the main issues as 5G networks evolve, and their implications for fault management. We describe the relevant machine learning techniques through to deep learning, and survey the progress which has been made in their application, based on the building blocks of a typical fault management system. We review recent work to develop the abilities of deep learning systems to explain and justify their recommendations to network operators. We discuss forthcoming changes in network architecture which are likely to impact fault management and offer a vision of how fault management systems can exploit deep learning in the future. We identify a series of research topics for further study in order to achieve this.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad
Authors: Mulvey, D., Foh, C. H., Imran, M. A., and Tafazolli, R.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Access
Publisher:IEEE
ISSN:2169-3536
ISSN (Online):2169-3536
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in IEEE Access 7:124514-124539
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300725Distributed Autonomous Resilient Emergency Management System (DARE)Muhammad ImranEngineering and Physical Sciences Research Council (EPSRC)Uncle 12187 - EP/P028764/ENG - Systems Power & Energy