Disturbances Prediction of Bit Error Rate for High-Speed Railway Balise Transmission Through Persistent State Mapping

Abstract

Effective and timely prediction of bit error rate (BER) disturbances is an important means to improve the safety of balise uplink transmission. However, the joint influence of multiple non-stationary factors makes it difficult to predict the abrupt changes of disturbances. To solve this problem, an adaptive attention-based bidirectional stateful encoder-decoder model is proposed for BER disturbances prediction. By persistently transferring the forward and backward states in a batch-to-batch mapping manner, the bidirectional stateful encoder can enhance the capture ability for disturbances. Additionally, an adaptive stateful decoder is used to dynamically synthesize the temporal correlation and contextual summarization to reduce the error accumulation in long-term forecasting. Through effective disturbance discrimination and accumulative error elimination, the proposed model can improve the overall prediction accuracy for BER disturbances. Experiment results on real-world high-speed railway datasets show that the proposed model can achieve superior performance than the state-of-the-art methods.