RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization
Angular radio maps describe the received-power distribution over the angle of arrival and underpin beam selection and receiver localization in sixth-generation (6G) networks. Predicting the angular power spectrum (APS) from geometry is difficult, because the mapping is ill-posed in non-line-of-sight (NLOS) conditions and must generalize to unseen environments. Distortion-minimizing regressors return the conditional mean, which over-smooths the spectrum and erases the multipath structure that downstream tasks need. We cast the task as a perception-distortion problem and propose RadioDiff-v2, a dual-branch one-dimensional diffusion transformer trained with flow matching. It couples periodic...
| Authors | Xiucheng Wang, Junxi Huang, Nan Cheng |
|---|---|
| Primary category | cs.IT |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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