A Gaussian Mixture Model for Mobile Location Prediction

Abstract

Location prediction is essential for efficient location management in mobile networks. In this paper, we propose a novel method for predicting the current location of a mobile user and describe how the method can be used to facilitate paging process. Based on observation that most mobile users have mobility patterns that they follow in general, the proposed method discovers common mobility patterns from a collection of user moving logs. To do this, the method models cell-residence times as generated from a mixture of Gaussian distributions and use the expectation maximization (EM) algorithm to learn the model parameters. Mobility patterns, each is characterized by a common trajectory and a cell-residence time model, are then used for making predictions. Simulation studies show that the proposed method has better prediction performance when compared with two other prediction methods.