In this paper, a novel multi-modal intelligent channel model for sixth-generation (6G) multiple-uncrewed aerial vehicle (multi-UAV)-to-multi-vehicle communications is proposed. To thoroughly explore the mapping relationship between the physical environment and the electromagnetic space in the complex multi-UAV-to-multi-vehicle scenario, two new parameters, i.e., terrestrial traffic density (TTD) and aerial traffic density (ATD), are developed and a new sensing-communication intelligent integrated dataset is constructed in suburban scenario under different TTD and ATD conditions. With the aid of sensing data, i.e., light detection and ranging (LiDAR) point clouds, the parameters of static scatterers, terrestrial dynamic scatterers, and aerial dynamic scatterers in the electromagnetic space, e.g., number, distance, angle, and power, are quantified under different TTD and ATD conditions in the physical environment. In the proposed model, the channel non-stationarity and consistency on the time and space domains and the channel non-stationarity on the frequency domain are simultaneously mimicked. The channel statistical properties, such as time-space-frequency correlation function (TSF-CF), time stationary interval (TSI), and Doppler power spectral density (DPSD), are derived and simulated. Simulation results match ray-tracing (RT) results well, which verifies the accuracy of the proposed multi-UAV-to-multi-vehicle channel model.

In this paper, a novel adaptive near-field channel model with an extremely large-scale multiple-input multiple-output (XL-MIMO) uniform planar array (UPA) is proposed for sixth generation (6G) multiple-uncrewed aerial vehicle (multi-UAV) cooperative communications. In the proposed model, a novel selective near-field area (SNA) of the XL-MIMO UPA, where the transmission is regarded as spherical wavefront, is proposed to balance complexity and accuracy of near-field channel modeling. To jointly model the non-stationarity on the array, and in the space, time, and frequency domains, an adaptive UPA-UAV-time-frequency non-stationary algorithm is developed, which mimics the non-stationarity on the XL-MIMO UPA for the first time. The channel parameters related to the three-dimensional (3D) continuously arbitrary trajectory and self-rotation of multi-UAVs are also taken into account in the proposed model and the developed algorithm. To explore the channel statistics and validate the proposed model, a new XL-MIMO-UPA-to-multi-UAV channel dataset at low-terahertz (low-THz) frequency band under National Stadium scenario is built. Key UPA-to-multi-UAV channel statistics, such as the array-space-time-frequency correlation function (ASTF-CF), time stationary interval (TSI), Doppler power spectral density (DPSD), and singular value spread (SVS), are obtained. The close agreement between the simulation results and ray-tracing results in National Stadium scenario is achieved, demonstrating the accuracy of proposed channel model.

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.