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Cell Fault Management Using Machine Learning Techniques

Mulvey, David, Foh, Chuan Heng, Imran, Muhammad Ali and Tafazolli, Rahim (2019) Cell Fault Management Using Machine Learning Techniques IEEE Access, 7. pp. 124514-124539.

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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: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Foh, Chuan
Imran, Muhammad
Date : 29 August 2019
Funders : Engineering and Physical Sciences Research Council (EPSRC)
DOI : 10.1109/ACCESS.2019.2938410
Copyright Disclaimer : © 2019 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2019.2938410, IEEE Acces
Uncontrolled Keywords : Cellular networks; Self healing; Cell outage; Cell degradation; Fault diagnosis; Deep learning; Explainable AI; Computer architecture; Microprocessors; Deep learning; Neural networks; Fault diagnosis; Cellular networks
Depositing User : Clive Harris
Date Deposited : 18 Sep 2019 15:12
Last Modified : 18 Sep 2019 15:16

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