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

2026-02-11

AI Summary

This work addresses the challenge of semantic communication in bandwidth-constrained low-altitude UAV downlink scenarios, where deep fading events in dynamic channels often cause complete reconstruction failure in conventional approaches. To mitigate this, the authors propose a structure–texture semantic disentanglement mechanism that decomposes content into deterministic structural components and stochastic textural details. Leveraging channel state prediction, they design a hierarchical transmission strategy that prioritizes the delivery of structural information during high-reliability time slots, thereby enabling differentiated semantic protection. The system integrates multi-stream variational encoding–decoding, a channel prediction model, and generative reconstruction techniques. Experimental results demonstrate that the proposed framework preserves structural integrity even under significant channel prediction mismatch, achieving a 5.6 dB gain in peak signal-to-noise ratio over a single-stream baseline.

Technology Category

• Machine Learning: Structured Learning
• Computer Vision: Learning & Optimization for CV
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

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.

Problem

• UAV
• semantic transmission
• channel impairment
• bandwidth scarcity
• DeepJSCC

Innovation

• semantic communication
• channel-aware scheduling
• generative reconstruction
• split-stream variational codec
• UAV downlink