Mobile User Trajectory Prediction Based on Machine Learning

Abstract

Ultra-dense network is the key technology of 5G. It provides mobile users with high transmission rates and efficient radio resource management. However, due to the dense deployment of base stations and the small coverage of a single base station in the ultra-dense network, user equipment may have to perform handovers more frequently. The current handover mechanism is more like a “passive handover” triggered by objective conditions. So frequent handover may degrade the user experience and affect the overall performance of the network. Therefore, it is necessary to predict the trajectory of the mobile user, and then take the prediction results into account to perform a more positive and intelligent handover. In response to this problem, in this paper, we propose an LSTM model that can predict a mobile user’s next location based on his historical trajectory and which cell he should connect to at the next moment. We use real mobile user trajectory data for training and testing. Reassuringly, it is best to achieve a prediction accuracy of 92.155% in the scenario we assumed.