A Knowledge Based Model for Analyzing Gsm Network Performance
A Knowledge-Based Model for Analyzing GSM Network Performance
Pasi Lehtimäki and Kimmo Raivio
Helsinki University of Technology Laboratory of Computer and Information Science P.O. Box 5400, FIN-02015 HUT, Finland
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.
1
Introduction
The radio resource management in current mobile communication networks concentrates on maximizing the number of users for which the services can be provided with required quality, while using only limited amount of resources [4]. Once the network is designed and implemented, the goal is to find a network configuration parameters that use the existing resources as efficiently as possible from the user point of view. In practice, this means that a reasonable tradeoff between the coverage and capacity of the network must be found. Good coverage allows users to initiate services at any location with acceptable service quality, while high capacity allows many network subscribers to use services simultaneously. However, improving the coverage tends to diminish the capacity and vice versa. A good tradeoff between coverage and capacity is obtained when the number of service denials (blocking) and abnormal service interruptions (dropping) are at the minimum, i.e the performance of the network is well optimized. In this paper, the
Pasi Lehtimäki and Kimmo Raivio
Helsinki University of Technology Laboratory of Computer and Information Science P.O. Box 5400, FIN-02015 HUT, Finland
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.
1
Introduction
The radio resource management in current mobile communication networks concentrates on maximizing the number of users for which the services can be provided with required quality, while using only limited amount of resources [4]. Once the network is designed and implemented, the goal is to find a network configuration parameters that use the existing resources as efficiently as possible from the user point of view. In practice, this means that a reasonable tradeoff between the coverage and capacity of the network must be found. Good coverage allows users to initiate services at any location with acceptable service quality, while high capacity allows many network subscribers to use services simultaneously. However, improving the coverage tends to diminish the capacity and vice versa. A good tradeoff between coverage and capacity is obtained when the number of service denials (blocking) and abnormal service interruptions (dropping) are at the minimum, i.e the performance of the network is well optimized. In this paper, the
References: ave busy tch 83 tch radio fail 74 dropped calls 74 tch radio fail 69 dropped calls 69 ave ul sig qual 8 69 ave ul sig qual 7 69 ave ul sig qual 11 69 ave ul sig qual 10 69 ave busy tch 69 ave busy tch 56 tch radio fail 7 dropped calls 7 ave ul sig qual 9 7 tch radio fail 6 dropped calls 6 ave ul sig qual 5 6 ave ul sig qual 4 6 ave ul sig qual 1 6 ave busy tch 6 ave busy tch 1 1. S. Mokhtar Bazaraa, D. Hanif Sherali, and C. M. Shetty. Nonlinear Programming: theory and algorithms. John Wiley and Sons, Inc., 1993. 2. Sofoklis A. Kyriazakos and George T. Karetsos. Practical Radio Resource Management in Wireless Systems. Artech House, Inc., 2004. 3. Pasi Lehtimäki and Kimmo Raivio. A SOM based approach for visualization of GSM network performance data. In Proceedings of the 18th Internation Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (to appear), 2005. 4. Jens Zander. Radio Resource Management for Wireless Networks. Artech House, Inc., 2001. Nokia Foundation is gratefully acknowledged for their financial support. (b) (a) (c) (d)