Abstract
Mobile cellular telecommunication networks are complex dynamic systems whose troubleshooting presents formidable challenges. Typically, the network performance analysis is carried out on a network cell basis and it is based on the traffic information obtained from various sensors such as the number of requested calls, number of dropped calls, number of handovers, etc. This paper presents a novel troubleshooting system, which provides likelihood of different user-specified root causes of performance degradation based on the observed sensory information and the underlying domain model. This domain model has a form of a Causal Network whose structure is appropriately chosen. The novelty of the herein presented approach is that the domain model is initially based on expert knowledge and later on refined via supervised learning with the data gathered during system operation.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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References
Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann Publishers, San Francisco (1998)
Obradovic, D., et al.: Detecting probability of occurrence of specific state of radio communications system using Bayes network probability theory model with at least one accessible state per process variable, patent EP1385348-A1, Siemens AG, Munich, Germany (2004)
Horn, J., Pellegrino, M., Lupas Scheiterer, R.: Method for creating a knowledge based causal or Bayesian network by carefully structuring base knowledge data so that the causal network can be automatically generated by a computer, patent WO2003005297-A2, Siemens AG, Munich, Germany (2004)
Horn, J., Birkhölzer, T., Hogl, O., Pellegrino, M., Lupas Scheiterer, R., Schmidt, K.-U., Tresp, V.: Knowledge Acquisition and Automated Generation of Bayesian Networks for a Medical Dialogue and Advisory System. In: Quaglini, S., Barahona, P., Andreassen, S. (eds.) AIME 2001. LNCS (LNAI), vol. 2101, pp. 199–202. Springer, Heidelberg (2001)
Heckerman, D.: A Tutorial on Learning with Bayesian Networks. In: Jordan, M. (ed.) Learning in Graphical Models, MIT Press, Cambridge (1999)
Lupas Scheiterer, R., Obradovic, D.: Bayesian Network Modeling Aspects Resulting from Applications in Medical Diagnosis and GSM Troubleshooting. In: Submitted to ICANN 2005, Krakow, Poland (2005)
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Obradovic, D., Scheiterer, R.L. (2005). Troubleshooting in GSM Mobile Telecommunication Networks Based on Domain Model and Sensory Information. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_116
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DOI: https://doi.org/10.1007/11550907_116
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