Vision-Assisted 3-D Predictive Beamforming for Green UAV-to-Vehicle Communications

Abstract

Unmanned aerial vehicles (UAVs) have demonstrated exceptional capabilities in various applications such as vehicle tracking, military monitoring and communication assistance. With their advantages in mobile communication and integrated multiple sensors, UAVs have great potential to obtain the integration gain through integrated sensing and communications (ISAC) designs. In this paper we focus on the beamforming algorithm for UAV-to-vehicle communications. To reduce the communication overhead of block transmission caused by the high mobility of the target vehicles and the UAV, we utilize the inherent vision functionality of the UAV platform and propose a vision-assisted beamforming framework. Specifically, to obtain the position of to-be-served vehicles, we employ YOLOv5, a deep learning model, for vehicle detection, followed by a Sage-Husa filter for multiple target vehicle tracking. Based on the predicted positions of the vehicles, we propose a lightweight beamforming algorithm to save communication overhead of beam tracking. To validate the effectivess of this work, we perform experiments on the UAVDT dataset for vehicle detection and tracking. The simulation results show that the proposed algorithm achieves significant performance gains on both the effective achievable rates and the energy efficiency.