{"ID":6267002,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-12T01:34:29.232725809Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.08045","arxiv_id":"2607.08045","title":"RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization","abstract":"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 angular encoding, adaptive layer-normalization conditioning, a Fourier angular mixer, and joint velocity and clean-signal heads. A per-metric estimator portfolio reads every deployment quantity from this single model, so that samples carry the distribution, the clean-signal head supplies a regression-grade point estimate, Bayes-optimal rules select beams, and the conditional likelihood localizes the receiver. We prove that a concentrated conditional yields a straight probability-flow trajectory that one step integrates exactly, identifying deterministic transport as the correct inductive bias. On a zero-shot test of 99 environments and one million links, RadioDiff-v2 leads every baseline on every metric, with a 0.39 dB Wasserstein-1 distance, per-bin error below the regression baseline, a 2.43 dB eight-beam NLOS sweep loss, and a 20.6-pixel localization error with four base stations. Code is available at https://github.com/UNIC-Lab/RadioDiff-v2.","short_abstract":"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 a...","url_abs":"https://arxiv.org/abs/2607.08045","url_pdf":"https://arxiv.org/pdf/2607.08045v1","authors":"[\"Xiucheng Wang\",\"Junxi Huang\",\"Nan Cheng\"]","published":"2026-07-09T01:48:47Z","proceeding":"cs.IT","tasks":"[\"cs.IT\",\"cs.LG\",\"eess.SP\"]","methods":"[\"Diffusion Model\",\"Transformer\"]","has_code":false,"code_links":[{"ID":614068,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-10T01:11:38.759438437Z","DeletedAt":null,"paper_id":6267002,"paper_url":"https://arxiv.org/abs/2607.08045","paper_title":"RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization","repo_url":"https://github.com/UNIC-Lab/RadioDiff-v2","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}