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

2026-03-24

AI Summary

This work addresses the challenge of accurately modeling rapidly time-varying air-to-ground channels induced by the high-speed motion of low Earth orbit (LEO) satellites, particularly at low elevation angles where existing models fall short due to their neglect of real-world geographical effects such as terrain blockage and vegetation absorption. By integrating digital elevation models and land cover data with ray tracing to identify line-of-sight/non-line-of-sight links and reflection paths, the study incorporates diffraction, vegetation attenuation, and atmospheric loss models to rigorously quantify environmental impacts on signal propagation. Furthermore, it proposes a generative diffusion-based framework to learn the mapping from environmental context to channel characteristics, enabling, for the first time, physically accurate and scalable real-time channel prediction. Experiments demonstrate that the method significantly outperforms conventional statistical models on both cellular and LEO satellite measurement datasets, achieving precise channel state prediction for arbitrary satellite–ground geometries.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• air-to-ground channel modeling
• environmental context
• LEO satellite
• terrain blockage
• wireless propagation

Innovation

• environment-aware channel modeling
• generative diffusion model
• ray tracing
• LEO satellite communications
• digital elevation model