Holistic Transmission Performance Prediction of Balise System with Gate-Steered Residual Interweave Networks

Accurate transmission performance prediction of the balise system is important for reliable ground–train communication in high-speed rails. However, the combined effects of ground–train coupling and on-board demodulation make it difficult to predict long-term and volatile holistic transmission performance under multisource disturbances. To address these issues, this article proposes a gate-steered residual interweave architecture. A new gated double-dilated temporal convolutional network (GDTCN) is introduced by combining large and small receptive fields with gating units to extract multiscale features and filter out useless information. It can learn multisource parameter disturbances induced by transient coupling and intermittent demodulation to reduce the prediction errors of occasional volatilities in holistic transmission performance. A new residual interweave GDTCN structure with linear gated cross connections is built to remit the differences between signal and telegram parameter features in view of the inconsistent data transfer modes during coupling and demodulation. This structure can facilitate multipath feature interaction and fusion to enrich discriminative correlation information, thereby improving the prediction accuracy of holistic transmission performance. An adaptive gated attention mechanism is designed to exploit correlation and dependent information between coupling and demodulation in a weighted fusion manner. It can minimize prediction error accumulation to enhance long-term holistic transmission performance forecasting. Extensive experiments demonstrate that the proposed architecture can perform predictions with high accuracy and efficiency under different rail conditions. The proposed method can provide early warning before ground balise or on-board module failures to improve the reliability and availability of ground–train communication.