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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. In this context, this paper proposes a system for auto-diagnosis in the Radio Access Network (RAN) of wireless systems. The malfunction of the RAN may be due not only to a hardware fault, but also (and more difficult to identify) to a bad configuration. The proposed system is based on the analysis of Key Performance Indicators (KPIs) in order to isolate the cause of the network malfunction. In this paper, two alternative probabilistic systems are compared, which differ on how KPIs are modelled (continuous or discrete variables). Experimental results are examined in order to support the theoretical concepts, based on data from a live network. The drawbacks and benefits of both systems are studied and some conclusions on the scenarios under which each model should be used are presented.
Wireless communication, Network Operations, Network management, Network monitoring, Probabilistic algorithms, Knowledge management applications, Decision support, Decision support, Knowledge modeling, Inference engines, Parameter learning, Engineering, Automation, Diagnostics

P. Lázaro, V. Wille, R. Barco and L. Díez, "Continuous versus Discrete Model in Autodiagnosis Systems for Wireless Networks," in IEEE Transactions on Mobile Computing, vol. 7, no. , pp. 673-681, 2008.
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