Low Earth Orbit (LEO) satellites are expected to be effective complements to terrestrial networks for the global seamless coverage. However, the fast mobility and long propagation delay of LEO satellites lead to the untimely transmission of the hand over signaling and even the radio link failures (RLFs), especially in high-speed terminals scenarios. Thus, we propose a novel conditional handover strategy based on trajectory prediction (PCHO) to eliminate the RLFs. Specifically, we first design a trajectory prediction model based on the gated recurrent unit (GRU) for high-speed terminals. We next optimize the conditional handover (CHO) scheme by utilizing the predicted trajectories of terminals to assist the CHO triggering. Simulation results show that the trajectory prediction model can realize accurate prediction, and the proposed conditional hand over strategy is able to effectively reduce the handover rate and improve the time-of-stay and the signal quality for terminals during handover.

A Conditional Handover Strategy Based on Trajectory Prediction for High-Speed Terminals in LEO Satellite Networks

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.

Mobile User Trajectory Prediction Based on Machine Learning

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Mobile User Trajectory Prediction Based on Machine Learning