SpaceDiffusion: Over-the-Orbit Diffusion for Space Generate-and-Forward Communications
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
Learned image compression (LIC) has achieved impressive rate-distortion performance. However, existing methods remain highly vulnerable to packet loss, a common challenge in satellite and emergency communications.
Satellites are emerging as promising platforms to extend generative \emph{artificial intelligence} (AI) services to remote areas lacking terrestrial infrastructure. However, deploying space generative...
arXiv:2609.01062v1 Announce Type: new Abstract: Satellites are emerging as promising platforms to extend generative \emph{artificial intelligence} (AI) services to remote areas lacking terrestrial in...
arXiv:2606. 03074v1 Announce Type: new Abstract: Diffusion models achieve high-fidelity radio map construction through iterative denoising, yet their sampling cost limits practicality in dynamic wireless systems where radio maps must be refreshed repeatedly.
arXiv:2410. 08238v2 Announce Type: replace-cross Abstract: We introduce NetDiff, a node-conditioned denoising diffusion model that generates directional link topologies and a two-slot transmit/receive parity for mobile ad hoc networks.
The paper introduces DRIFT, a lightweight framework for joint channel estimation and prediction in low Earth orbit non-terrestrial networks, aiming to reduce pilot overhead by using data-driven processing after the initial slot. DRIFT refines data-aided channel estimates and forecasts future channel responses with low computational cost, offering two variants based on convolutional and LSTM layers. Simulations show up to 12% spectral efficiency gain over conventional pilot-based systems, with under 200k multiply-accumulate operations suitable for on-board satellite implementation.