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.
UAV Swarm Enabled Aerial Movable Antenna System for Low-Altitude Economy: From Far-Field to Near-Field Communication
Nanjing University of Science and Technology（南京理工大学）；State Key Laboratory of Internet of Things for Smart City（智慧城市物联网国家重点实验室）；University of Macau（澳门大学）；National Mobile Communications Research Laboratory（移动通信国家重点实验室）；Southeast University（东南大学）；Department of Information Engineering, the Chinese University of Hong Kong（香港中文大学信息工程系）；Purple Mountain Laboratories（紫金山实验室）；School of information Science and Engineering, Southeast University（东南大学信息科学与工程学院）；Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电气与计算机工程系）
