Data analysis methods for cellular network performance optimization

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Modern cellular networks including GSM/GPRS and UMTS networks offer faster and more versatile communication services for the network subscribers. As a result, it becomes more and more challenging for the cellular network operators to enhance the usage of available radio resources in order to meet the expectations of the customers. Cellular networks collect vast amounts of measurement information that can be used to monitor and analyze the network performance as well as the quality of service. In this thesis, the application of various data-analysis methods for the processing of the available measurement information is studied in order to provide more efficient methods for performance optimization. In this thesis, expert-based methods have been presented for the monitoring and analysis of multivariate cellular network performance data. These methods allow the analysis of performance bottlenecks having an effect in multiple performance indicators. In addition, methods for more advanced failure diagnosis have been presented aiming in identification of the causes of the performance bottlenecks. This is important in the analysis of failures having effect on multiple performance indicators in several network elements. Finally, the use of measurement information in selection of most useful optimization action have been studied. In order to obtain good network performance efficiently, the expected performance of the alternative optimization actions must be possible to evaluate. In this thesis, methods to combine measurement information and application domain models are presented in order to build predictive regression models that can be used to select the optimization actions providing the best network performance.
cellular network, radio network, radio resource optimization, information visualization, regression, clustering, segmentation, optimization
  • [Publication 1]: Pasi Lehtimäki, Kimmo Raivio, and Olli Simula. Mobile Radio Access Network Monitoring Using the Self-Organizing Map. In Proceedings of the 10th European Symposium on Artificial Neural Networks (ESANN 2002), Bruges, Belgium, April 24-26, 2002, pages 231-236.
  • [Publication 2]: Jaana Laiho, Kimmo Raivio, Pasi Lehtimäki, Kimmo Hätönen, and Olli Simula. Advanced Analysis Methods for 3G Cellular Networks. IEEE Transactions on Wireless Communications, volume 4, number 3, pages 930-942, May 2005.
  • [Publication 3]: Pasi Lehtimäki, Kimmo Raivio, and Olli Simula. Self-Organizing Operator Maps in Complex System Analysis. In Proceedings of the Joint 13th International Conference on Artificial Neural Networks and 10th International Conference on Neural Information Processing (ICANN/ICONIP 2003), Istanbul, Turkey, June 26-29, 2003. Lecture Notes in Computer Science, volume 2714, pages 622-629.
  • [Publication 4]: Pasi Lehtimäki and Kimmo Raivio. A SOM Based Approach for Visualization of GSM Network Performance Data. In Proceedings of the 18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE 2005), Bari, Italy, June 22-25, 2005. Lecture Notes in Artificial Intelligence, volume 3533, pages 588-598.
  • [Publication 5]: Pasi Lehtimäki and Kimmo Raivio. A Knowledge-Based Model for Analyzing GSM Network Performance. In Proceedings of the 6th International Symposium on Intelligent Data Analysis (IDA 2005), Madrid, Spain, September 8-10, 2005. Lecture Notes in Computer Science, volume 3646, pages 204-215.
  • [Publication 6]: Pasi Lehtimäki and Kimmo Raivio. Combining Measurement Data and Erlang-B Formula for Blocking Prediction in GSM Networks. In Proceedings of the 10th Scandinavian Conference on Artificial Intelligence (SCAI 2008), Stockholm, Sweden, May 26-28, 2008, accepted for publication. © 2008 by authors.
  • [Publication 7]: Pasi Lehtimäki. A Model for Optimisation of Signal Level Thresholds in GSM Networks. International Journal of Mobile Network Design and Innovation, accepted for publication.