Geometry-Based Stochastic Line-of-Sight Probability Model for A2G Channels under Urban Scenarios

TL;DR

This paper introduces a stochastic LoS probability model for urban air-to-ground channels that accounts for geographic and environmental factors, providing accurate predictions across different frequencies and altitudes.

Contribution

It develops a general 3D LoS probability model based on geographic info and Fresnel zones, with a machine learning-based parametric approximation for efficiency.

Findings

Model aligns well with existing models at low altitude.

Performance improves at higher altitudes compared to ray-tracing simulations.

Analytical model aids in channel performance analysis like coverage and outage.

Abstract

Line-of-sight (LoS) path is essential for the reliability of air-to-ground (A2G) communications, but the existence of LoS path is difficult to predict due to random obstacles on the ground. Based on the statistical geographic information and Fresnel clearance zone, a general stochastic LoS probability model for three-dimensional (3D) A2G channels under urban scenarios is developed. By considering the factors, i.e., building height distribution, building width, building space, carrier frequency, and transceiver's heights, the proposed model is suitable for different frequencies and altitudes. Moreover, in order to get a closed-form expression and reduce the computational complexity, an approximate parametric model is also built with the machine-learning (ML) method to estimate model parameters. The simulation results show that the proposed model has good consistency with existing models at the low altitude. When the altitude increases, it has better performance by comparing with that of the ray-tracing Monte-Carlo simulation data. The analytical results of proposed model are helpful for the channel modeling and performance analysis such as cell coverage, outage probability, and bit error rate in A2G communications.