Integrated Sensing and Communications for End-to-End Predictive Beamforming Design in Vehicle-to-Infrastructure Networks

Integrated sensing and communications (ISAC) has emerged as a promising technology for predictive beamforming in vehicle-to-infrastructure (V2I) networks. Most of the existing works on ISAC assume each vehicle is equipped with a single antenna and use a two-phase scheme for predictive beamforming design. In the first phase, the reflected sensing signals at the roadside unit (RSU) are used to estimate the state parameters (e.g., angle, channel state information (CSI)) of the vehicles. In the second phase, the beamformer is predicted based on the estimated state parameters. The two-phase scheme suffers from the drawback that the estimation error in the first phase can impact the beamformer design in the second phase and may lead to a degradation in the achievable rate. In this work, we design predictive beamformers for both the RSU and vehicles in an end-to-end manner by using deep learning. We propose one-sided predictive beamforming (OSPB) and two-sided predictive beamforming (TSPB) schemes, where the beamformers for the vehicles are determined by the RSU and by the vehicles themselves, respectively. Both schemes directly predict the beamformers based on the reflected sensing signals via deep neural networks (DNNs). Compared with the existing two-phase schemes, the proposed schemes bypass the intermediate parameter estimation phase, thereby mitigating the impact of parameter estimation error. Our simulation results demonstrate the advantages of the proposed schemes over the two-phase baseline schemes in terms of achievable sum-rate.