Combining local and global profiles for mobility prediction in LTE femtocells
We propose in this paper a mobility prediction model based on the notions of local and global mobile-user profiles. The local profiles are associated with a mobile user and correspond to its frequent and similar movements, whereas the global profiles match with the frequent and similar movements of the majority of users in the covered area. We consider the LTE network architecture with possible deployment of femtocells. The prediction model combines two complementary algorithms: the global profiles-based algorithm and the local profiles-based one. The former is implemented in the enhanced Node B and the home enhanced Node B and the latter works at the user terminal level. An algorithmic approach is used to identify such local and global profiles from real cellular network datasets and we show how to use them for an efficient mobility prediction. Simulation results show that our approach is significantly efficient in predicting both random and regular movements.