Hierarchical mobility prediction in the future heterogeneous networks for the tradeoff between accuracy and complexity

Mobility prediction is one of the promising technologies for improving quality of service(QoS) and network resource utilization. In future heterogeneous networks(HetNets), the network topology will become extremely complicated due to the widespread deployment of different types of small-cell base stations(SBSs). For this complex network topology, traditional mobility prediction methods may cost unacceptable overhead to maintain high prediction accuracy. This problem is studied in this paper, and the hierarchical mobility prediction scheme(HMPS) is proposed for the future HetNets. By dividing the entire process into two prediction stages with different granularity, the tradeoff between prediction accuracy and computational complexity is investigated. Before performing prediction of user mobility, some frequently visited locations are identified from the user’s trajectory, and each location represents an important geographic area(IGA). In the coarse-grained prediction phase, the next most possible location to be visited is predicted at the level of the possible geographic areas by using a second-order Markov chain with fallback. Then, the fine-grained prediction of user position is performed based on hidden Markov model(HMM) from temporal and spacial dimensions. Simulation results demonstrate that, compared with the existing prediction methods, the proposed HMPS can achieve a good compromise between prediction accuracy and complexity.