Learning of model parameters for fault diagnosis in wireless networks

Authors:
Raquel Barco;Volker Wille;Luis Díez;Matías Toril
Affiliations:
Dept. Ingeniería de Comunicaciones, E.T.S.I. Telecomunicación, University of Málaga, Málaga, Spain;Nokia Siemens Networks, Consulting & Systems Integration, Huntingdon, Cambridge, UK;Dept. Ingeniería de Comunicaciones, E.T.S.I. Telecomunicación, University of Málaga, Málaga, Spain;Dept. Ingeniería de Comunicaciones, E.T.S.I. Telecomunicación, University of Málaga, Málaga, Spain
Venue:
Wireless Networks
Year:
2010

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Abstract

Self-management is essential for Beyond 3G (B3G) systems, where the existence of multiple access technologies (GSM, GPRS, UMTS, WLAN, etc.) will complicate network operation. Diagnosis, that is, fault identification, is the most difficult task in automatic fault management. This paper presents a probabilistic system for auto-diagnosis in the radio access part of wireless networks, which comprises a model and a method. The parameters of the model are thresholds for the discretization of Key Performance Indicators (KPIs) and probabilities. In this paper, some techniques are proposed for the automatic learning of those model parameters. In order to support the theoretical concepts, experimental results are examined, based on data from a live network. It has been proven that calculating parameters from network statistics, instead of being defined by diagnosis experts, highly increases the performance of the diagnosis system. In addition, the proposed techniques enhance the results obtained with continuous diagnosis models previously exposed in the literature.