Deep Learning for ISAC-Enabled End-to-End Predictive Beamforming in Vehicular Networks

Integrated sensing and communications (ISAC) has emerged as a promising technology for predictive beamforming design in vehicle-to-infrastructure (V2I) networks. Most of the existing works use a two-step approach for predictive beamforming design. The first step is to estimate the state parameters of a vehicle (e.g., angle of arrival (AoA), channel state information (CSI)) from the received sensing signal samples at the road side unit (RSU). The second step is to determine the beamforming vector based on the estimated parameters. However, estimation errors may be introduced in the first step which impacts the subsequent beamforming design and leads to degradation in the achievable rate. In this work, by using deep learning, we propose an ISAC-enabled end-to-end predictive beamforming (E2E-PB) approach to obtain the beamforming vector directly from the reflected signal samples. The proposed approach does not require an intermediate state parameters estimation step. We develop an attention-based long short-term memory (LSTM) network to capture the temporal correlation in the reflected signal samples and determine the beamformer. The network is trained in an unsupervised manner to maximize the achievable rate. We compare our proposed E2E-PB approach with two state-of-the-art schemes, namely, the extended Kalman filtering framework and a deep learning based two-step approach. The results show that our proposed E2E-PB approach obtains a higher achievable rate than the other two baseline schemes, and has close performance when compared with the optimal beamforming design with perfect CSI.