@article{CHEN2026, 
author = {Yong CHEN and Xuan TAO and Chen XIE},
title = {Time Synchronization Method for Train-to-Train Communication Based on Variational Mode Decomposition and TA-BLSTM Network},
year = {2026},
journal = {Journal of South China University of Technology (Natural Science Edition)},
volume = {54},
number = {4},
pages = {101-109},
keywords = {5G-R communication, train-to-train communication, time synchronization, variational mode decomposition, temporal attention mechanism, bidirectional long short-term memory network},
url = {https://www.sciopen.com/article/10.12141/j.issn.1000-565X.250162},
doi = {10.12141/j.issn.1000-565X.250162},
abstract = {Train-to-train communication constitutes the foundational architecture of China’s railway-dedicated 5G-R communication systems, and the time synchronization for train-to-train communication is crucial for train operation safety. To address the issue of poor time synchronization performance caused by non-stationary wireless channels and transmission delays in train-to-train communication, this paper proposes a time synchronization approach based on variational mode decomposition (VMD) and bidirectional long short-term memory (BLSTM) network incorporating temporal attention (TA) mechanisms. In this study, first, a 5G-R train-to-train communication clock model is established by analyzing the delay errors in train-to-train communication. Next, the VMD model is employed to decompose the train-to-train communication time series into intrinsic mode functions with different frequencies, thus isolating noise elements and increasing the signal-to-noise ratio. Then, noise-dominated components are identified by calculating energy values, and wavelet soft thresholding is applied to denoise these components, thus enhancing the quality of train-to-train communication synchronization time series. Finally, a TA-BLSTM network is proposed, which integrates a temporal attention mechanism into a bidirectional LSTM framework. This network extracts long-term temporal features from the train-to-train time synchronization sequence using BLSTM, and dynamically captures temporal dependencies via the temporal attention mechanism, thus enabling high-precision prediction and dynamic compensation of time synchronization deviations in train-to-train communication, and furthermore, achieving accurate time synchronization. Simulation experiments demonstrate that the proposed approach can effectively achieve train-to-train time synchronization in both relay and non-relay communication scenarios. As compared with some existing methods, the proposed approach significantly reduces time synchronization offsets and offers faster convergence speed as well as greater stability in train-to-train time synchronization process.}
}