针对低空无人机传输难题，分发结构与纹理信息并智能调度，提升图像重建鲁棒性。
Robust Semantic Transmission for Low-Altitude UAVs: Predictive Channel-Aware Scheduling and Generative Reconstruction
- 将图像分为结构和纹理两部分，分别处理
- 相比单流方法，峰值信噪比提升5.6 dB
- 适合对可靠性要求高的低空无人机视觉应用
无人飞行器（UAV）下行链路传输支持关键的实时视觉应用，但受制于带宽稀缺和动态信道干扰。空地（A2G）链路快速波动导致可靠传输时隙间歇出现，未来信道质量仅能以不确定方式预测。传统深度联合源信道编码（DeepJSCC）传输耦合特征流，当特定时隙发生深衰落时会导致全局重建失败。通过将语义内容解耦为确定性结构成分与随机纹理成分，实现与信道可靠性匹配的差异化错误保护策略。本文提出一种预测性传输框架，采用分流变分编解码器与信道感知调度器，优先在可靠时隙中传输结构布局信息。实验表明，该方法相比单流基线实现5.6 dB的峰值信噪比增益，并在显著预测偏差下仍保持结构保真度。
原文摘要 · Abstract (English)
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.
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