A diffusion-model-based posterior sampler, trained on simulated fluid antenna channels, achieves lower normalized mean squared error than compressed sensing baselines for 2D FAS channel estimation and is accelerated by skipping sampling steps.
Sparse baye sian learning-based channel estimation for fluid antenna system s,
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Accurate and Fast Channel Estimation for Fluid Antenna Systems with Diffusion Models
A diffusion-model-based posterior sampler, trained on simulated fluid antenna channels, achieves lower normalized mean squared error than compressed sensing baselines for 2D FAS channel estimation and is accelerated by skipping sampling steps.