Anomaly detection and self-healing in industrial wireless networks

Zoha, A. , Abbasi, Q. H. and Imran, M. A. (2019) Anomaly detection and self-healing in industrial wireless networks. In: Imran, M. A., Hussain, S. and Abbasi, Q. H. (eds.) Wireless Automation as an Enabler for the Next Industrial Revolution. Wiley-IEEE Press, pp. 109-138. ISBN 9781119552611 (doi:10.1002/9781119552635.ch6)

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Publisher's URL: https://www.wiley.com/en-gb/9781119552611

Abstract

A self‐healing block in self‐organizing network consists of two modules, namely cell outage detection and cell outage compensation (COC). This chapter presents a data‐driven analytics framework for autonomous outage detection and coverage optimization in an LTE network that exploits the minimization of drive test functionality as specified by 3GPP in Release 10. The outage detection approach first learns a normal profile of the network behaviour by projecting the network measurements to a low‐dimensional space. For this purpose, the multi‐dimensional scaling method in conjunction with domain and density based detection models, one class support vector machine based detector and local outlier factor based detector, respectively, are examined for different network conditions. The low‐dimensional representation of network measurements facilitates data modelling and allows the anomaly detection algorithms to obtain a better estimation of data density. To optimize the coverage and capacity of the identified outage zone, a fuzzy based reinforcement learning algorithm for COC is proposed.

Item Type:Book Sections
Status:Published
Glasgow Author(s) Enlighten ID:Zoha, Dr Ahmed and Abbasi, Dr Qammer and Imran, Professor Muhammad
Authors: Zoha, A., Abbasi, Q. H., and Imran, M. A.
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
Publisher:Wiley-IEEE Press
ISBN:9781119552611
Published Online:27 December 2019

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