{"ID":2893942,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.12166","arxiv_id":"2507.12166","title":"RadioDiff-3D: A 3D$\\times$3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication","abstract":"Radio maps (RMs) serve as a critical foundation for enabling environment-aware wireless communication, as they provide the spatial distribution of wireless channel characteristics. Despite recent progress in RM construction using data-driven approaches, most existing methods focus solely on pathloss prediction in a fixed 2D plane, neglecting key parameters such as direction of arrival (DoA), time of arrival (ToA), and vertical spatial variations. Such a limitation is primarily due to the reliance on static learning paradigms, which hinder generalization beyond the training data distribution. To address these challenges, we propose UrbanRadio3D, a large-scale, high-resolution 3D RM dataset constructed via ray tracing in realistic urban environments. UrbanRadio3D is over 37$\\times$3 larger than previous datasets across a 3D space with 3 metrics as pathloss, DoA, and ToA, forming a novel 3D$\\times$33D dataset with 7$\\times$3 more height layers than prior state-of-the-art (SOTA) dataset. To benchmark 3D RM construction, a UNet with 3D convolutional operators is proposed. Moreover, we further introduce RadioDiff-3D, a diffusion-model-based generative framework utilizing the 3D convolutional architecture. RadioDiff-3D supports both radiation-aware scenarios with known transmitter locations and radiation-unaware settings based on sparse spatial observations. Extensive evaluations on UrbanRadio3D validate that RadioDiff-3D achieves superior performance in constructing rich, high-dimensional radio maps under diverse environmental dynamics. This work provides a foundational dataset and benchmark for future research in 3D environment-aware communication. The dataset is available at https://github.com/UNIC-Lab/UrbanRadio3D.","short_abstract":"Radio maps (RMs) serve as a critical foundation for enabling environment-aware wireless communication, as they provide the spatial distribution of wireless channel characteristics. Despite recent progress in RM construction using data-driven approaches, most existing methods focus solely on pathloss prediction in a fix...","url_abs":"https://arxiv.org/abs/2507.12166","url_pdf":"https://arxiv.org/pdf/2507.12166v1","authors":"[\"Xiucheng Wang\",\"Qiming Zhang\",\"Nan Cheng\",\"Junting Chen\",\"Zezhong Zhang\",\"Zan Li\",\"Shuguang Cui\",\"Xuemin Shen\"]","published":"2025-07-16T11:54:08Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"eess.SY\"]","methods":"[\"Diffusion Model\"]","has_code":false,"code_links":[{"ID":612081,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2893942,"paper_url":"https://arxiv.org/abs/2507.12166","paper_title":"RadioDiff-3D: A 3D$\\times$3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication","repo_url":"https://github.com/UNIC-Lab/UrbanRadio3D","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}