A conditional diffusion model learns beam alignment priors to guide efficient top-k sweeps, achieving Hit@1 of 0.61 and improving over deterministic baselines by 180% on simulated data.
Diffusion- based generative prior for low-complexity mimo channel estimation
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Diffusion-Based Generative Priors for Efficient Beam Alignment in Directional Networks
A conditional diffusion model learns beam alignment priors to guide efficient top-k sweeps, achieving Hit@1 of 0.61 and improving over deterministic baselines by 180% on simulated data.