Sensing-Enabled Predictive Beamforming Design for RIS-Assisted V2I Systems: A Deep Learning Approach

Vehicle-to-infrastructure (V2I) communications have been regarded as an emerging application in next-generation wireless networks. However, guaranteeing high-quality wireless communications in high-mobility scenarios remains a major challenge. In this paper, we investigate the deployment of reconfigurable intelligent surface (RIS) for improving the communication performance of V2I systems. In particular, integrated sensing and communication (ISAC) signals are exploited to facilitate sensing-assisted beamforming. Aiming at maximizing the achievable rate, two deep learning-based predictive beamforming mechanisms are proposed. First, a two-stage beamforming design is devised, where the channel state information (CSI) is estimated based on the echo signals and predicted by a dedicated neural network for time-varying channels. Then, the transmit beamforming vector at the base station (BS) and the reflect beamforming matrix at the RIS are jointly optimized. To further reduce the computational complexities, we develop an end-to-end beamforming design by employing the parameter sharing mechanism and weighted loss function. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate that approaches the upper bound exploiting perfect CSI. In particular, the end-to-end design exhibits remarkable robustness against the impact of noise and achieves outstanding sensing-assisted beamforming performance, especially at the low signal-to-noise ratio region.

I. Introduction

Vehicle-to-infrastructure (V2I) communications, as a significant application in the sixth-generation mobile communication network (6G), has developed over the past decade [2], [3]. Consequently, the associated emerging applications, such as real-time video transmission, three-dimensional high-definition maps, and multimedia entertainment, impose strict requirements for communication quality of service (QoS) in terms of both latency and throughput [4]. Thanks to the availability of large bandwidth, millimeter wave (mmWave) technology can provide low-latency and high-speed data transmission up to multi-Gbps, thus offering great promises [5], [6]. Nevertheless, compared with the signals in low-frequency bands, mmWave signals encounter the fundamental issue of severe propagation and penetration losses. In practice, a higher transmit power is always required to compensate for the tremendous path attenuation to ensure sufficient data rate and board transmission range at the cost of low energy efficiency.