Diffusion-Driven Terahertz Air-Ground Communications under Dynamic Atmospheric Turbulence
Jinhao Yi, Weijun Gao, Chong Han

TL;DR
This paper introduces an AI-based framework for THz air-ground communications that models turbulence effects using fluid dynamics and optimizes transmission parameters to improve link capacity in dynamic atmospheric conditions.
Contribution
It develops a fluid-dynamics-informed turbulence model and an adaptive optimization algorithm for THz AG communications, addressing turbulence effects explicitly for the first time.
Findings
Turbulence-induced attenuation ranges from 18 to 28 dB at 0.7 Mach.
The proposed framework improves capacity by up to 66.5%.
Achieves 98% of the theoretical capacity limit.
Abstract
The ever-increasing demand for ultra-high data rates in space-air-ground integrated networks (SAGINs) has rendered terahertz THz communications a promising technology owing to its exceptionally broad and continuous spectrum resources. Nevertheless, in air-ground (AG) scenarios, the high mobility of aircraft induces intense and rapidly fluctuating turbulence, leading to additional propagation loss that is often overlooked in existing studies. To bridge this gap, this paper presents an AI-empowered THz AG communication framework that explicitly models turbulence-induced attenuation through fluid dynamics and integrates it into an adaptive optimization paradigm for communication performance enhancement. Specifically, a fluid-dynamics-informed attenuation model is established to characterize aircraft-generated turbulence and quantify its impact on THz signal propagation. Building upon this…
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Taxonomy
TopicsUAV Applications and Optimization · Millimeter-Wave Propagation and Modeling · Optical Wireless Communication Technologies
