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Context With the rapidly growing demand for cellular networks’ capacityand coverage, effective planning of Network Infrastructure (NI) has been amajor challenge for the telecom operators. The mobility patterns of different subscriber groups in the networks have been found to be a crucialaspect in the planning of NI. For a telecom operator, it is important to havean estimate of the efficiency (in terms of the Network Capacity - numberof subscribers that the network can handle) of the existing NI. For thispurpose, Lundberg et. al., have developed an optimization based strategycalled as Tetris Strategy (TS), based on the standard subscriber groupingapproach called MOSAIC. The objective of TS is to calculate the upperbound estimate of the efficiency of the NI. Objectives The major objective of this thesis is to compare the efficiencyvalue of the NI when the subscribers are grouped (clustered) based on theirmobility patterns (characterized by a mobile trajectory) with the efficiencyvalue obtained when the subscribers are grouped based on the standardsubscriber grouping approach - MOSAIC. Methods Literature Review (LR) has been conducted to identify the stateof the art similarity/distance measures and algorithms to cluster trajectory data. Among the identified ones, for conducting experiments, LongestCommon Subsequences has been chosen as a similarity/distance measure,and Spectral and Agglomerative clustering algorithms have been chosen.All the experiments have been conducted on the subscriber trajectory dataprovided by the telecom operator, Telenor. The clusters obtained from theexperiments have been plugged into TS, to calculate the upper bound estimate of the efficiency of the NI. Results For the highest radio cell capacity, the network capacity valuesfor Spectral clustering, Agglomerative clustering and MOSAIC groupingsystem are 207234, 148056 and 87584 respectively. For every radio cellcapacity value, the mobility based clusters resulted in a higher network efficiency values than the MOSAIC. However, both spectral and agglomerativealgorithms have generated a very low quality clusters with the silhouettescores of 0.0717 and 0.0543 respectively. Conclusions Based on the analysis of the results, it can be concluded that,mobility based grouping of subscribers in the cellular network provide highernetwork efficiency values compared to the standard subscriber grouping systems such as MOSAIC.

[pt] Com o advento dos sistemas celulares de segunda e terceira gerações é esperado que as operadoras se vejam obrigadas a enfrentar um aumento dramático na carga de sinalização que trafega sobre a parte fixa da rede móvel. Apesar disso, são raros os provedores de serviços de comunicações móveis que possuem atualmente a capacidade de prever com relativa precisão o montante desse aumento. Este trabalho apresenta as etapas do desenvolvimento de uma ferramenta de simulação para análise de desempenho de redes de comunicação móvel celular com base na carga de sinalização gerada pelos procedimentos que a mantém em operação. A plataforma de simulação inclui um modelo de mobilidade e teletráfego para caracterizar o processo de geração dos cenários típicos de uma rede móvel celular e um modelo de retardos para representação da rede de sinalização. Ao final do estudo,são apresentados exemplos de aplicação da ferramenta na obtenção de resultados sobre gerência de status, gerência de localização, avaliação da carga de sinalização,dimensionamento da rede de sinalização e análise de desempenho para diferentes configurações de rede. / [en] The advent of second and third generation cellular systems make cellular operators face dramatic increase in the signaling traffic over the fixed part of the mobile network. In spite of this, rare mobile communications service providers are able to forecast the above mention increase and quantify it with reasonable precision. This work describes the development process of a simulation tool for performance analysis of cellular mobile network based on the signaling load generated by the procedures that keeps it working. The simulation platform inlcudes a mobility and teletraffic model to describe the generation process of cellular mobile networks tipical scenarios and a delay model to represent the signaling network. At the end, examples showing the application of the simulation tool to obtain results about status and location management, signaling load evaluation, signaling network planning and performance analysis for different network configurations are presented.