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.
RadioNet: Robust deep-learning based radio fingerprinting,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.IT 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.