Predictive beamforming design is an essential task in realizing high-mobility integrated sensing and communication (ISAC), which highly depends on the accuracy of the channel prediction (CP), i.e., predicting the angular parameters of users. However, the performance of CP highly depends on the estimated historical channel stated information (CSI) with estimation errors, resulting in the performance degradation for most traditional CP methods. To further improve the prediction accuracy, in this paper, we focus on the ISAC in vehicle networks and propose a convolutional long-short term (CLSTM) recurrent neural network (CLRNet) to predict the angle of vehicles for the design of predictive beamforming. In the developed CLRNet, both the convolutional neural network (CNN) module and the LSTM module are adopted to exploit the spatial features and the temporal dependency from the estimated historical angles of vehicles to facilitate the angle prediction. Finally, numerical results demonstrate that the developed CLRNet-based method is robust to the estimation error and can significantly outperform the state-of-the-art benchmarks, achieving an excellent sum-rate performance for ISAC systems.

中文翻译：

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用于集成传感和通信的车载网络中的预测波束成形的深度 CLSTM

预测波束成形设计是实现高移动性集成传感与通信（ISAC）的一项重要任务，它高度依赖于信道预测（CP）的准确性，即预测用户的角度参数。然而，CP 的性能高度依赖于估计的历史信道状态信息（CSI），具有估计误差，导致大多数传统 CP 方法的性能下降。为了进一步提高预测精度，在本文中，我们关注车辆网络中的 ISAC，并提出了一种卷积长短期（CLSTM）循环神经网络（CLRNet）来预测车辆的角度，用于预测波束成形的设计。在已开发的 CLRNet 中，采用卷积神经网络（CNN）模块和LSTM模块来利用估计的车辆历史角度的空间特征和时间依赖性，以促进角度预测。最后，数值结果表明，所开发的基于 CLRNet 的方法对估计误差具有鲁棒性，并且可以显着优于最先进的基准，从而为 ISAC 系统实现出色的总和率性能。