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Wireless user-mobility prediction has been investigated from various angles to improve network performance. Student populations in campuses, pedestrian and vehicular movement in urban areas, etc have been studied by cell phone and mobility management researchers to address issues in quality of service (QoS), seamless session handoffs, etc. Access to information such as user movement times, direction, speed, etc provides an opportunity for networks to efficiently manage resources to satisfy user needs. Towards this goal, we propose a generic framework to approach the problem of mobility prediction using hidden Markov models (HMM). This method can be used to model hidden parameters in the models. We propose a way to extract user movement information from a real dataset, train a HMM using this data and make predictions using the HMM. This model can successfully predict long sequences of a …