Robust Semantic Transmission for Low-Altitude UAVs: Predictive Channel-Aware Scheduling and Generative Reconstruction

TL;DR

This paper introduces a robust semantic transmission framework for low-altitude UAVs that leverages predictive channel-aware scheduling and generative reconstruction, significantly improving visual data delivery under dynamic channel conditions.

Contribution

It proposes a novel decoupling of semantic content into deterministic and stochastic components, enabling differentiated error protection and a predictive transmission strategy for UAV downlink communications.

Findings

Achieves 5.6 dB SNR gain over baselines.

Maintains structural fidelity despite channel prediction errors.

Enhances reliability of visual data transmission in UAV networks.

Abstract

Unmanned aerial vehicle (UAV) downlink transmission facilitates critical time-sensitive visual applications but is fundamentally constrained by bandwidth scarcity and dynamic channel impairments. The rapid fluctuation of the air-to-ground (A2G) link creates a regime where reliable transmission slots are intermittent and future channel quality can only be predicted with uncertainty. Conventional deep joint source-channel coding (DeepJSCC) methods transmit coupled feature streams, causing global reconstruction failure when specific time slots experience deep fading. Decoupling semantic content into a deterministic structure component and a stochastic texture component enables differentiated error protection strategies aligned with channel reliability. A predictive transmission framework is developed that utilizes a split-stream variational codec and a channel-aware scheduler to prioritize the delivery of structural layout over reliable slots. Experimental evaluations indicate that this approach achieves a 5.6 dB gain in peak signal-to-noise (SNR) ratio over single-stream baselines and maintains structural fidelity under significant prediction mismatch.