发表机构
The University of Hong Kong; The Hong Kong University of Science and Technology（香港大学; 香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出SpaceDiffusion，一种基于信道失真感知扩散的卫星生成-转发框架，通过将失真校正项融入DDIM更新，无需重训即可适应丢包与压缩，实现低延迟、省15dB功率的图像传输。
AI 中文摘要
卫星通信是第六代（6G）移动网络的重要组成部分，为全球服务提供无处不在的连接。然而，卫星上行链路仍然是地面设备的关键瓶颈：其有限的发射功率和天线孔径导致低数据速率和高数据包错误。为了克服这一瓶颈，本文提出了一种称为生成-转发（GF）通信的新型中继范式，其中卫星利用在轨生成式人工智能（AI）在转发前稳健地重建损坏的数据。具体而言，我们提出了SpaceDiffusion，一种用于卫星辅助图像传输的轨道外扩散框架。该框架的核心是采用以下方法开发的信道失真感知扩散理论。通过将压缩和丢失的图像令牌的恢复表述为逆问题，该理论将信道失真校正项直接纳入传统的去噪扩散隐式模型（DDIM）更新中。因此，这种设计使得单个预训练扩散模型能够动态适应不同的数据包丢失模式和压缩失真，而无需重新训练。此外，我们分析性地刻画了渐进式令牌重建误差，并推导了一个扩散步激活阈值，该阈值预测SpaceDiffusion何时有望优于传统的解码-转发（DF）中继。基于这些理论见解，我们进一步开发了一种能量感知的提前退出策略，以在轨道上高效部署SpaceDiffusion。实验结果表明，与采用重传协议的DF方案相比，SpaceDiffusion实现了更低的端到端延迟，并在目标感知质量下节省了约15 dB的上行链路发射功率。
英文摘要
Satellite communications are an essential component of sixth-generation (6G) mobile networks, which provide ubiquitous connectivity for global services. However, the satellite uplink remains a critical bottleneck for ground devices: their limited transmit power and antenna apertures result in low data rates and high packet errors. To overcome this bottleneck, this paper advocates a novel relaying paradigm termed generate-and-forward (GF) communications, where satellites exploit on-orbit generative artificial intelligence (AI) to robustly reconstruct corrupted data prior to forwarding. Specifically, we propose SpaceDiffusion, an over-the-orbit diffusion framework for satellite-assisted image transmission. The core of this framework is a channel-distortion-aware diffusion theory developed using the following approach. By formulating the recovery of compressed and lost image tokens as an inverse problem, this theory incorporates a channel-distortion correction term directly into the conventional denoising diffusion implicit model (DDIM) update. As a result, this design enables a single pretrained diffusion model to adapt dynamically to varying packet-loss patterns and compression distortions without retraining. Furthermore, we analytically characterize the progressive token-reconstruction error and derive a diffusion-step activation threshold that predicts when SpaceDiffusion is expected to outperform conventional decode-and-forward (DF) relaying. Building on these theoretical insights, we further develop an energy-aware early-exit policy to efficiently deploy SpaceDiffusion in orbit. Experimental results demonstrate that SpaceDiffusion achieves lower end-to-end latency compared to DF scheme with retransmission protocol and saves approximately 15 dB of uplink transmit power at a target perceptual quality.
