High-mobility scenarios in next-generation wireless networks, such as those involving vehicular communications, require ultra-reliable and low-latency communications (URLLC). However, rapidly time-varying channels pose significant challenges to traditional OFDM-based systems due to the Doppler effect and channel aging. Orthogonal time frequency space (OTFS) modulation offers resilience by representing channels in the quasi-static delay-Doppler (DD) domain. This letter proposes a novel channel prediction framework for OTFS systems using a hybrid convolutional neural network and transformer (CNN-Transformer) architecture. The CNN extracts compact features that exploit the DD-domain sparsity of the channel matrices, while the transformer models temporal dependencies with causal masking for consistency. Simulation experiments under extreme $500$ \si{km/h} mobility conditions demonstrate that the proposed method outperforms state-of-the-art baselines, reducing the root mean square error and mean absolute error by $12.2\%$ and $9.4\%$, respectively. These results demonstrate the effectiveness of DD-domain representations and the proposed model in accurately predicting channels in high-mobility scenarios, thereby supporting the stringent URLLC requirements in future wireless systems.

翻译：下一代无线网络中的高移动性场景（例如涉及车辆通信的场景）要求超可靠低时延通信（URLLC）。然而，由于多普勒效应和信道老化，快速时变的信道对传统的基于OFDM的系统构成了重大挑战。正交时频空间（OTFS）调制通过在准静态时延-多普勒（DD）域中表示信道，提供了鲁棒性。本文提出了一种用于OTFS系统的新型信道预测框架，该框架采用混合卷积神经网络与Transformer（CNN-Transformer）架构。CNN提取利用信道矩阵DD域稀疏性的紧凑特征，而Transformer则通过因果掩码对时间依赖性进行建模以确保一致性。在极端$500$ \si{km/h}移动性条件下的仿真实验表明，所提方法优于现有最先进的基线方法，将均方根误差和平均绝对误差分别降低了$12.2\%$和$9.4\%$。这些结果证明了DD域表示和所提模型在高移动性场景下准确预测信道的有效性，从而支持未来无线系统中严格的URLLC要求。