A Composite Shadow Fading Model for Low-Altitude Communication Scenarios

This paper proposes a general shadow fading model for low-altitude communication scenarios. The proposed model captures real-world characteristics by incorporating altitude-dependent landscape shadowing (LS), and accounting for the unmanned aerial vehicle (UAV) posture variations and structural influences on airframe shadowing (AS). A machine learning (ML)-based approach is employed to optimize the generation of altitude-dependent LS parameters, considering the random altitudes of both the transmitter and receiver. Additionally, UAV three-dimensional posture variations and structural features are integrated into the AS modeling method. Numerical simulations validate that landscape shadowing depends on the altitude of transceivers, and airframe shadowing is notably influenced by UAV posture and structural attributes.