The efficient dimensioning of cellular wireless access networks depends highly on the accuracy of the underlying mathematical models of user distribution and traffic estimations. Mobility prediction also considered as an effective method contributing to the accuracy of IP multicast based multimedia transmissions, and ad hoc routing algorithms. In this paper we focus on the tradeoff between the accuracy and the complexity of the mathematical models used to describe user movements in the network. We propose mobility model extension, in order to utilize user's movement history thus providing more accurate results than other widely used models in the literature. The new models are applicable in real-life scenarios, because these rely on additional information effectively available in cellular networks (e.g. handover history), too. The complexity of the proposed models is analyzed, and the accuracy is justified by means of simulation.

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Recent wireless networks offer more bandwidth than ever. High quality services are develfoped and provided by the operators, the number of users is constantly increasing. As the transferred data and the number of terminals are growing, the network providers have to face the increasing complexity of the network management and operation tasks. In this paper we observe the estimation and prediction of users' distribution in a cellular network. We compare the accuracy of mobility models serving for location prediction with our enhanced user motion prediction algorithm.

Nowadays, in the wireless networks the number of users and the transferred packet switched data are increasing dramatically. Due to the demands and the market competition the services are becoming more complex, therefore network providers and operators are facing even more difficult network management and operation tasks. The efficient network dimensioning and configuration highly depend on the underlying mathematical model of user distribution and expected data transfer level. In this paper we propose a Markov Movement-model Creator Framework (MMCF) for setting up a model based on the network parameters and requirements with optimal number of states. Firstly we describe a method that gives an abstract model of the mobile network and the node, and we introduce a simple classifying method that defines the necessary parameters of the exact Markov movement model. The mathematical solutions for determining these parameters are also presented in the paper. Finally we analyze the accuracy, complexity and usability of the proposed MMCF and an analytical comparison is made with other mobility models, the comparison is justified with simulations. The movement model created with the framework helps the network operators in setting up an effective authorization, fraud detection system or solving self-configurations issues.