# RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization arXiv: https://arxiv.org/abs/2607.08045 · HTML: https://mlsanity.com/paper/2607.08045/ ## Key facts - No citing papers in the map yet (data through 14 Oct 2026). - Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models. - Builds on 33 of its 46 references that are in the map. - First posted on arXiv 9 Jul 2026. ## Placed in - Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/) ## Builds on (most cited first, 30 of 33) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020) - [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022) - [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022) - [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022) - [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022) - [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021) - [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022) - [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017) - [The Perception-Distortion Tradeoff](https://mlsanity.com/paper/1711.06077/) (2017) - [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019) - [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019) - [RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction](https://mlsanity.com/paper/2408.08593/) (2024) - [RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network](https://mlsanity.com/paper/2212.12817/) (2022) - [RadioDiff-k2: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction](https://mlsanity.com/paper/2504.15623/) (2025) - [RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks](https://mlsanity.com/paper/2403.16397/) (2024) - [RadioDiff-3D: A 3D×3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication](https://mlsanity.com/paper/2507.12166/) (2025) - [Generative AI on SpectrumNet: An Open Benchmark of Multiband 3D Radio Maps](https://mlsanity.com/paper/2408.15252/) (2024) - [Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases](https://mlsanity.com/paper/2405.20568/) (2024) - [RadioDiff-Inverse: Diffusion Enhanced Bayesian Inverse Estimation for ISAC Radio Map Construction](https://mlsanity.com/paper/2504.14298/) (2025) - [LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning](https://mlsanity.com/paper/2202.00738/) (2022) - [Radio Map Estimation -- An Open Dataset with Directive Transmitter Antennas and Initial Experiments](https://mlsanity.com/paper/2402.00878/) (2024) - [A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awareness](https://mlsanity.com/paper/2603.17499/) (2026) - [RadioDiff-Flux: Efficient Radio Map Construction via Generative Denoise Diffusion Model Trajectory Midpoint Reuse](https://mlsanity.com/paper/2601.02790/) (2026) - [Exploiting Radio Fingerprints for Simultaneous Localization and Mapping](https://mlsanity.com/paper/2305.13635/) (2023) - [Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaverses](https://mlsanity.com/paper/2505.12710/) (2025) ## Abstract (arXiv) 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.