arXiv AI By Jianhao Huang, Zhanwei Wang, Khaled B. Letaief, Kaibin Huang

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

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arXiv Machine Learning
Aug 4

NetDiff: Graph Diffusion with Improved Global Capabilities to Generate and Update Mobile Network Topologies

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.

By F\'elix Marcoccia, Victor Fagoo, Gilles Monzat, C\'edric Adjih, Thomas Watteyne, Paul M\"uhlethaler
arXiv AI
Aug 25

DRIFT: Joint Channel Estimation and Prediction Towards Pilotless 6G Non-Terrestrial 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.

By Bruno De Filippo, Carla Amatetti, Alessandro Vanelli-Coralli