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A Knowledge-Based Model for Analyzing GSM Network Performance

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Advances in Intelligent Data Analysis VI (IDA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3646))

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Abstract

In this paper, a method to analyze GSM network performance on the basis of massive data records and application domain knowledge is presented. The available measurements are divided into variable sets describing the performance of the different subsystems of the GSM network. Simple mathematical models for the subsystems are proposed. The model parameters are estimated from the available data record using quadratic programming. The parameter estimates are used to find the input-output variable pairs involved in the most severe performance degradations. Finally, the resulting variable pairs are visualized as a tree-shaped cause-effect chain in order to allow user friendly analysis of the network performance.

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References

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Author information

Authors and Affiliations

  1. Laboratory of Computer and Information Science, Helsinki University of Technology,HUT, P.O. Box 5400, FIN-02015, Finland

    Pasi Lehtimäki & Kimmo Raivio

Authors
  1. Pasi Lehtimäki
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  2. Kimmo Raivio
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Editor information

Editors and Affiliations

  1. Institute for Information Technology, National Research Council Canada, Ottawa, Canada

    A. Fazel Famili

  2. LIACS, Leiden University, The Netherlands

    Joost N. Kok

  3. IFM, Linköping University, SE-58183, Linköping, Sweden

    José M. Peña

  4. Department of Computer Science, Universiteit Utrecht,  

    Arno Siebes

  5. Utrecht University, TB Utrecht,, P.O. box 80 089, NL-3508, the Netherlands

    Ad Feelders

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© 2005 Springer-Verlag Berlin Heidelberg

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Lehtimäki, P., Raivio, K. (2005). A Knowledge-Based Model for Analyzing GSM Network Performance. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552253_19

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  • DOI: https://doi.org/10.1007/11552253_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28795-7

  • Online ISBN: 978-3-540-31926-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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