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Abstract:Keywords:Mobile networks produce a huge amount of spatia-temporal data. The data consists of parameters of base stations and quality information of calls. The Self-Organizing Map (SOM) is an efficient tool for visualization and clustering of multidimensional data. It transforms the input vectors on two-dimensional grid of prototype vectors and orders them. The ordered prototype vectors are easier to visualize and explore than the original data. There are two possible ways to start the analysis. We can build either a model of the network using state vectors with parameters from all mobile cells or a general one cell model trained using one cell state vectors from all cells. In both methods further analysis is needed. In the first method the distributions of parameters of one cell can be compared with the others and in the second it can be compared how well the general model represents each cell.Neural networks, self-organizing map, cellular network, performance optimisation. IntroductionAs the launch of third generation technology approaches, operators are forming strategies for the deployment of their networks. These strategies must be supported by realistic business plans both in terms of future service demand estimates and the requirement for investment in network infrastructure.When provisioning 3G services the control for the access part can be divided into three levels. Two lowest layers are radio resource management (RRM) functionalities and the highest hierarchy level is control performed by the network management system (NMS). More about this control hierarchy can be found in [10]. The scope of this paper is the NMS level. The role of NMS is essential owing to the fact that major enhancements or new service roll-outs are planned by utilizing the measured long term performance data from existing network.The multidimensional performance space in future cellular networks force the tra-

A neural network based clustering method for the analysis of soft handovers in 3G network is introduced. The method is highly visual and it could be utilized in explorative analysis of mobile networks. In this paper, the method is used to find groups of similar mobile cell pairs in the sense of handover measurements. The groups or clusters found by the method are characterized by the rate of successful handovers as well as the causes of failing handover attempts. The most interesting clusters are those which represent certain type of problems in handover attempts. By comparing variable histograms of a selected cluster to histograms of the whole data set an application domain expert may find some explanations on problems. Two clusters are investigated further and causes of failing handover attempts are discussed.

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