A Bayesian Regression Based LTE-R Handover Decision Algorithm for High-Speed Railway Systems

Abstract

In this paper, we propose a machine learning based handover decision algorithm for high-speed railway systems. In a standard LTE-R network, a mobile relay (MR) on a train connects a DeNB and various onboard devices, including train controllers. The standard LTE-R handover decision scheme assumes the moving node may move back and forth. Thus, it makes the serving DeNB keep the inferior connection with the MR during the predefined Time-To-Triger, even though the MR gets closer to the cell boundary and can make a better connection with the neighbor DeNB. However, a train keeps moving forward and never moves backward during the operation. Thus, it is expected that improved quality of wireless connection can be attained if the serving DeNB starts the handover of the MR earlier than the standard by predicting the cell boundary crossing time. Especially, the Gaussian Bayesian regression model outputs the predictive distribution that contains prediction values as well as information of their uncertainty. Thus, we can decide whether to accept the prediction according to the predictive distribution. Based on the prediction, the serving DeNB decides the handover initiation time to make the MR cross the cell boundary during the handover execution. We evaluate the performance of handover decision algorithm through simulations. The simulation results show that the proposed scheme outperforms the standard handover decision algorithm and the H2-based scheme in terms of communication reliability at cell boundary regardless of the moving speed.