A Multi-Modal UAV-to-Ground Channel Model for 6G Intelligent Sensing-Communication Integration

In this paper, a novel multi-modal uncrewed aerial vehicle (UAV)-to-ground channel model is proposed for sixth-generation (6G) intelligent sensing-communication integration, where communication information and sensing data, i.e., light detection and ranging (LiDAR) point cloud data, are integrated. To thoroughly explore the mapping relationship between the electromagnetic space and the physical environment in UAV-to-ground scenarios, a new intelligent sensing-communication integrated UAV-to-ground dataset with channel information and LiDAR point clouds is constructed in an urban scenario under low, medium, and high vehicular traffic density (VTD) conditions. By detecting dynamic and static objects in the physical environment with LiDAR point clouds, scatterers in the electromagnetic space are divided into dynamic and static scatterers. In addition, the parameters of dynamic and static scatterers in the electromagnetic space, e.g., number, distance, angle, and power, are quantified under different VTDs in the physical environment. In the proposed model, time non-stationarity and consistency along time axis and the frequency non-stationarity at different frequencies are simultaneously mimicked. The channel statistical properties, such as time-frequency correlation function and Doppler power spectral density (DPSD), are derived. Since simulation results match ray-tracing (RT) results well, the accuracy of the proposed multi-modal UAV-to-ground channel model in urban scenarios is validated.