A discrete denoising diffusion model learns from probing histories to generate promising beam candidates, yielding better SNR, lower beam-miss probability, and reduced probe regret than baselines under tight probing budgets.
The Complexity of Markov Decision Processes,
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Discrete Diffusion for Codebook-Based Beam Candidate Generation
A discrete denoising diffusion model learns from probing histories to generate promising beam candidates, yielding better SNR, lower beam-miss probability, and reduced probe regret than baselines under tight probing budgets.