A flow-matching 1D diffusion transformer predicts angular radio maps from building geometry, achieving distributional fidelity, per-bin accuracy, beam selection, and Bayesian localization from a single zero-shot model across 99 environments.
Generative ai on spectrumnet: An open benchmark of multiband 3d radio maps,
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RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization
A flow-matching 1D diffusion transformer predicts angular radio maps from building geometry, achieving distributional fidelity, per-bin accuracy, beam selection, and Bayesian localization from a single zero-shot model across 99 environments.