Non-Stationary THz UAV Air-to-Ground Propagation MIMO Channel Modeling with Sensing-Communication Shared Clusters and Adaptive Sensing-Assisted Transmission

Terahertz (THz) integrated sensing and communication (ISAC) is a pivotal paradigm for enabling high-rate, ultra-reliable air-to-ground (A2G) connectivity in sixth-generation (6G) unmanned aerial vehicle (UAV) networks. From a physical propagation and environment-aware transmission perspective, current THz channel models inadequately characterize dynamic obstacle scattering, neglect sensing-communication shared clusters, and lack closed-loop frameworks leveraging sensing to assist communication. To bridge these gaps, this paper proposes a novel 3D non-stationary geometry-based stochastic model (GBSM) for THz UAV A2G MIMO channels operating at 300 GHz. The model explicitly incorporates obstacle-induced scattering with Radar Cross Section (RCS)-dependent properties, employing single-point models for small obstacles and multi-point models for large obstacles to capture multipath structures, Doppler effects, molecular absorption, and dynamic cluster evolution. Furthermore, we develop a shared cluster identification framework utilizing delay-angle similarity metrics combined with the Hungarian algorithm to match background scatterers with target-induced paths. Building upon this physical model, a closed-loop sensing-assisted adaptive transmission scheme is established, integrating obstacle-trajectory-driven Kalman channel prediction, affected subchannel avoidance, and waterfilling power allocation. Simulation results demonstrate that the proposed framework accurately characterizes physical THz propagation environments and significantly improves system capacity and reliability, demonstrating the efficacy of environment-level sensing-assisted communication in dynamic THz UAV scenarios.