Multi-Modal Sensing Data-Based Real-Time Path Loss Prediction for 6G UAV-to-Ground Communications

In this letter, a multi-modal sensing data based real-time path loss prediction scheme for sixth-generation (6G) unmanned aerial vehicle (UAV)-to-ground communications is developed. Meanwhile, a new mixed multi-modal sensing and communication integration dataset in the UAV-to-ground scenario is constructed. Based on the constructed dataset, the mapping relationship between physical space and electromagnetic space is explored, and the multi-modal sensing data based real-time path loss prediction scheme is developed. Simulation results show that the proposed scheme outperforms 3GPP UMa non-line-of-sight (NLoS) and slope-intercept models. By comparing simulation and ray-tracing (RT)-based results, the utility of the proposed scheme is further verified.