Scalable Air-to-Ground Wireless Channel Modeling Using Environmental Context and Generative Diffusion

TL;DR

This paper introduces an environment-aware, scalable channel modeling approach for air-to-ground links that combines real geographic data, ray tracing, and diffusion models to accurately predict channel behavior in dynamic environments.

Contribution

It presents a novel method integrating environmental data and diffusion models to efficiently simulate realistic air-to-ground wireless channels, enabling real-time analysis.

Findings

Model accurately predicts channel performance in real environments.

Validation with cellular and satellite data confirms effectiveness.

Diffusion model reduces computational complexity for real-time use.

Abstract

The fast motion of Low Earth Orbit (LEO) satellites causes the propagation channel to vary rapidly, and its behavior is strongly shaped by the surrounding environment, especially at low elevation angles where signals are highly susceptible to terrain blockage and other environmental effects. Existing studies mostly rely on assumed statistical channel distributions and therefore ignore the influence of the actual geographic environment. In this paper, we propose an environment-aware channel modeling method for air-to-ground wireless links. We leverage real environmental data, including digital elevation models (DEMs) and land cover information, together with ray tracing (RT) to determine whether a link is line-of-sight (LOS) or non-line-of-sight (NLOS) and to identify possible reflection paths of the signal. The resulting obstruction and reflection profiles are then combined with models of diffraction loss, vegetation absorption, and atmospheric attenuation to quantitatively characterize channel behavior in realistic geographic environments. Since RT is computationally intensive, we use RT-generated samples and environmental features to train a scalable diffusion model that can efficiently predict channel performance for arbitrary satellite and ground terminal positions, thereby supporting real-time decision-making. In the experiments, we validate the proposed model with measurement data from both cellular and LEO satellite links, demonstrating its effectiveness in realistic environments.