关键词: 5G-R通信, 车车通信, 时间同步, 变分模态分解, 注意力双向长短期记忆网络

Abstract:

Train-to-train communication constitutes the foundational architecture for China’s railway-dedicated 5G-R communication systems, and achieving 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 an enhanced time synchronization approach based on variational mode decomposition combined with a bidirectional long short-term memory network incorporating time attention mechanisms. First, a 5G-R train-to-train communication clock model is established by analyzing the delay errors in train-to-train communication. Then, the VMD model is employed to decompose the train-to-train communication time series into intrinsic mode functions of different frequencies, thus isolating noise elements and enhancing the signal-to-noise ratio. Next, noise-dominated components are identified by calculating energy values, and wavelet soft thresholding is applied to denoise these components, enhancing the quality of the train to train communication synchronization time series. Finally, a TA-BLSTM network is proposed, which integrates a time attention mechanism into a bidirectional LSTM framework. This network extracts long-term temporal features from the train to train time synchronization sequence using the bidirectional LSTM, while the time attention mechanism dynamically captures temporal dependencies, enabling high-precision prediction and dynamic compensation of time synchronization deviations in train to train communication, thus achieving accurate time synchronization. Simulation experiments demonstrate that the proposed method can effectively achieve train-to-train time synchronization in both relay and non-relay communication scenarios. Compared with other methods, the proposed approach significantly reduces synchronization offset errors and offers faster convergence speed and greater stability during the train to train time synchronization process.

Key words: 5G-R communication, train-to-train communication, time synchronization, variational mode decomposition, attention bidirectional long short term memory network