To guarantee excellent reliability performance in ultra-reliable low-latency communications (URLLC), pragmatic precoder design is an effective approach. However, an efficient precoder design highly depends on the accurate instantaneous channel state information at the transmitter (ICSIT), which however, is not always available in practice. To overcome this problem, in this paper, we focus on the orthogonal time frequency space (OTFS)-based URLLC system and adopt a deep learning (DL) approach to directly predict the precoder for the next time frame to minimize the frame error rate (FER) via implicitly exploiting the features from estimated historical channels in the delay-Doppler domain. By doing this, we can guarantee the system reliability even without the knowledge of ICSIT. To this end, a general precoder design problem is formulated where a closed-form theoretical FER expression is specifically derived to characterize the system reliability. Then, a delay-Doppler domain channels-aware convolutional long short-term memory (CLSTM) network (DDCL-Net) is proposed for predictive precoder design. In particular, both the convolutional neural network and LSTM modules are adopted in the proposed neural network to exploit the spatial-temporal features of wireless channels for improving the learning performance. Finally, simulation results demonstrated that the FER performance of the proposed method approaches that of the perfect ICSI-aided scheme.

翻译：为确保超可靠低延迟通信（URLLC）中的卓越可靠性性能，实用的预编码器设计是一种有效方法。然而，高效的预编码器设计高度依赖于发射机处准确的瞬时信道状态信息（ICSIT），但在实际中该信息并非始终可用。为解决此问题，本文聚焦于基于正交时频空（OTFS）的URLLC系统，采用深度学习（DL）方法直接预测下一时间帧的预编码器，通过隐式利用延迟-多普勒域中估计的历史信道特征来最小化帧错误率（FER）。通过这种方式，即使没有ICSIT知识，也能保证系统可靠性。为此，我们构建了一个通用预编码器设计问题，其中专门推导了闭式理论FER表达式以表征系统可靠性。随后，提出了一种延迟-多普勒域信道感知卷积长短期记忆网络（DDCL-Net）用于预测性预编码器设计。特别地，所提出的神经网络同时采用卷积神经网络和LSTM模块，以利用无线信道的时空特征来提升学习性能。最后，仿真结果表明，所提方法的FER性能可接近完美ICSI辅助方案的水平。