A Data-Driven Multi-Height Empirical LoS Probability Model for Urban A2G Channels

Line-of-sight (LoS) probability modeling plays an essential role in the reliability determination of millimeter wave (mmWave) communication systems. However, since most LoS probability models do not consider altitude information of communication terminals, they cannot be directly applied to air-to-ground (A2G) communication scenarios. In this paper, we propose a new multi-height LoS probability model for mmWave unmanned aerial vehicle (UAV) communication scenarios. A machine learning (ML)-based parameter estimation method is also developed, which trains the data from the constructed virtual urban scenes. We first propose a LoS/none-LoS (NLoS) identification method to recognize the LoS path and calculate the LoS probability. Then, we construct a two-layer single-input multiple-output back propagation neural network (BPNN) which trains the relationship between the model parameters and the altitude of UAVs. Simulation results show that the proposed LoS probability model has a good consistency with the ray tracing (RT) simulation data and the currently existing models when altitudes of UAVs are low. As the altitude increases, our model is still applicable and can achieve an excellent agreement with the RT data.