Knowledge acquisition for diagnosis model in wireless networks

Authors:
Raquel Barco;Pedro Lázaro;Volker Wille;L. Díez;Sagar Patel
Affiliations:
Departamento Ingeniería de Comunicaciones, University of Málaga, E.T.S.I. Telecomunicación, Campus Universitario de Teatinos s/n, E-29071 Málaga, Spain;Departamento Ingeniería de Comunicaciones, University of Málaga, E.T.S.I. Telecomunicación, Campus Universitario de Teatinos s/n, E-29071 Málaga, Spain;Nokia Siemens Networks, Consulting and Systems Integration, Ermine Business Park, Huntingdon, Cambridge PE29 6YJ, UK;Departamento Ingeniería de Comunicaciones, University of Málaga, E.T.S.I. Telecomunicación, Campus Universitario de Teatinos s/n, E-29071 Málaga, Spain;Nokia Siemens Networks, Consulting and Systems Integration, Ermine Business Park, Huntingdon, Cambridge PE29 6YJ, UK
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

In the near future, several radio access technologies will coexist in Beyond 3G mobile networks (B3G) and they will be eventually transformed into one seamless global communication infrastructure. Self-managing systems (i.e. those that self-configure, self-protect, self-heal and self-optimize) are the solution to tackle the high complexity inherent to these networks. This paper proposes a probabilistic model for self-healing in the radio access network (RAN) of wireless systems. The main difficulty in model construction is that, contrary to other application domains, in wireless networks there are no databases of previously classified cases from which to learn the model parameters. Due to this reason, in this paper, a knowledge acquisition procedure is proposed to build the model from the knowledge of troubleshooting experts. In order to support the theoretical concepts, a model has been built and it has been tested in a live network, proving the feasibility of the proposed system. Additionally, a knowledge-based model has been compared to a data-based model, showing the benefits of the former when the number of training cases is scarce.