# SpaceDiffusion: Over-the-Orbit Diffusion for Space Generate-and-Forward Communications

arXiv: https://arxiv.org/abs/2609.20899 · HTML: https://mlsanity.com/paper/2609.20899/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 19 of 19)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Posterior Sampling for General Noisy Inverse Problems](https://mlsanity.com/paper/2209.14687/) (2022)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024)
- [Satellite Communications in the New Space Era: A Survey and Future Challenges](https://mlsanity.com/paper/2002.08811/) (2020)
- [Token Communications: A Large Model-Driven Framework for Cross-modal Context-aware Semantic Communications](https://mlsanity.com/paper/2502.12096/) (2025)
- [D2-JSCC: Digital Deep Joint Source-channel Coding for Semantic Communications](https://mlsanity.com/paper/2403.07338/) (2024)
- [Semantic Satellite Communications Based on Generative Foundation Model](https://mlsanity.com/paper/2404.11941/) (2024)
- [Revisiting Outage for Edge Inference Systems](https://mlsanity.com/paper/2504.03686/) (2025)
- [Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2508.15185/) (2025)
- [ResiComp: Loss-Resilient Image Compression via Dual-Functional Masked Visual Token Modeling](https://mlsanity.com/paper/2502.10812/) (2025)
- [Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies](https://mlsanity.com/paper/2504.01676/) (2025)
- [SpaceMoE: Realizing Distributed Mixture-of-Experts Inference over Space Networks](https://mlsanity.com/paper/2605.00515/) (2026)
- [5G NB-IoT via low density LEO Constellations](https://mlsanity.com/paper/2108.06172/) (2021)
- [Generative Feature Imputing -- A Technique for Error-resilient Semantic Communication](https://mlsanity.com/paper/2508.17957/) (2025)

## Abstract (arXiv)

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.