This paper investigates deep learning techniques to predict transmit beamforming based on only historical channel data without current channel information in the multiuser multiple-input-single-output downlink. This will significantly reduce the channel estimation overhead and improve the spectrum efficiency especially in high-mobility vehicular communications. Specifically, we propose a joint learning framework that incorporates channel prediction and power optimization, and produces prediction for transmit beamforming directly. In addition, we propose to use the attention mechanism in the Long Short-Term Memory Recurrent Neural Networks to improve the accuracy of channel prediction. Simulation results using both a simple autoregressive process model and the more realistic 3GPP spatial channel model verify that our proposed predictive beamforming scheme can significantly improve the effective spectrum efficiency compared to traditional channel estimation and the method that separately predicts channel and then optimizes beamforming.

翻译：本文研究在多用户多输入单输出下行链路中，仅基于历史信道数据而非当前信道信息，利用深度学习技术预测发射波束赋形的方法。该方法能显著降低信道估计开销并提升频谱效率，尤其适用于高机动性车载通信场景。具体而言，我们提出了一种联合学习框架，该框架融合了信道预测与功率优化，并直接生成发射波束赋形的预测结果。此外，我们提出在长短期记忆循环神经网络中引入注意力机制以提高信道预测精度。采用简单自回归过程模型和更真实的3GPP空间信道模型进行的仿真验证表明，与传统的信道估计方法以及先单独预测信道再优化波束赋形的方法相比，我们提出的预测性波束赋形方案能显著提升有效频谱效率。