  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.
