Abstract:

In the process of high-speed railway train operation,in order to maintain uninterrupted communication,the train needs to constantly carry out handover with the base station.As a key technology of LTE-R communication,handover is crucial to ensuring traffic safety.Aiming at the low success rate of handover in the next generation high-speed railway LTE-R wireless communication system due to the fixed hysteresis threshold parameters,a high-speed railway handover algorithm based on the LSTM recurrent neural network is proposed.First,by using the memory characteristics of the LSTM neural network and the temporal and spatial correlation characteristics of high-speed railway handover overlapping area signals,a deep learning network for dynamic prediction of handover hysteresis threshold parameters based on the LSTM recurrent neural network is constructed.Second,through the proposed LSTM deep learning model,the handover hysteresis parameters are trained offline and predicted online to obtain the handover threshold value at the future time,which realizes the adaptive prediction of handover hysteresis parameters during high-speed train driving,and overcomes the disadvantage of fixed hysteresis threshold parameters.Finally,simulation results show that the proposed method can effectively improve the handover success rate and reduce the impact of the ping-pong handover rate compared with the traditional A3 algorithm and other comparison algorithms.Research results provide a certain theoretical reference for high-speed railway traffic safety and LTE-R evolution.

Key words: handover, long-term evolution for railway, LSTM recurrent neural network, handover prediction, high-speed railway