Path probability prediction model for 5G UAV communication scenarios

In the 5G air-to-ground (A2G) communication scenarios, the variable altitude and the changing scattering environment of unmanned aerial vehicles (UAVs) lead to the dynamic birth and death of the UAV communication propagation path. Therefore, path probability prediction is essential to describe the dynamic birth and death of propagation path and build the accurate A2G channel models. Considering the geometric information of the 3D scattering environment and the Fresnel zone, a line-of-sight (LoS) probability model is proposed in this paper based on the Poisson-distributed A2G urban scenarios, which is related to altitude and frequency. On this basis, we propose a ground specular (GS) path probability model for the first time in this paper using the method of mirroring the incident path. Simulation results show that the LoS and GS path probability models in this paper show good agreement with the average probability of massive ray tracing (RT) simulations at different communication altitudes and frequencies. Furthermore, the proposed LoS probability model is compatible with the existing standard models.