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Learning of model parameters for fault diagnosis in wireless networks

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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.

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Acknowledgements

This work has been partially supported by the Spanish Ministry of Science and Technology under project TIC2003-07827.

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Authors and Affiliations

  1. Dept. Ingeniería de Comunicaciones, E.T.S.I. Telecomunicación, University of Málaga, Málaga, Spain

    Raquel Barco, Luis Díez & Matías Toril

  2. Nokia Siemens Networks, Consulting & Systems Integration, Huntingdon, Cambridge, UK

    Volker Wille

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  1. Raquel Barco
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  2. Volker Wille
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  3. Luis Díez
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  4. Matías Toril
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Correspondence to Raquel Barco.

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Barco, R., Wille, V., Díez, L. et al. Learning of model parameters for fault diagnosis in wireless networks. Wireless Netw 16, 255–271 (2010). https://doi.org/10.1007/s11276-008-0128-z

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