{"ID":2836801,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.20015","arxiv_id":"2511.20015","title":"iRadioDiff: Physics-Informed Diffusion Model for Indoor Radio Map Construction and Localization","abstract":"Radio maps (RMs) serve as environment-aware electromagnetic (EM) representations that connect scenario geometry and material properties to the spatial distribution of signal strength, enabling localization without costly in-situ measurements. However, constructing high-fidelity indoor RMs remains challenging due to the prohibitive latency of EM solvers and the limitations of learning-based methods, which often rely on sparse measurements or assumptions of homogeneous material, which are misaligned with the heterogeneous and multipath-rich nature of indoor environments. To overcome these challenges, we propose iRadioDiff, a sampling-free diffusion-based framework for indoor RM construction. iRadioDiff is conditioned on access point (AP) positions, and physics-informed prompt encoded by material reflection and transmission coefficients. It further incorporates multipath-critical priors, including diffraction points, strong transmission boundaries, and line-of-sight (LoS) contours, to guide the generative process via conditional channels and boundary-weighted objectives. This design enables accurate modeling of nonstationary field discontinuities and efficient construction of physically consistent RMs. Experiments demonstrate that iRadioDiff achieves state-of-the-art performance in indoor RM construction and received signal strength based indoor localization, which offers effective generalization across layouts and material configurations. Code is available at https://github.com/UNIC-Lab/iRadioDiff.","short_abstract":"Radio maps (RMs) serve as environment-aware electromagnetic (EM) representations that connect scenario geometry and material properties to the spatial distribution of signal strength, enabling localization without costly in-situ measurements. However, constructing high-fidelity indoor RMs remains challenging due to the...","url_abs":"https://arxiv.org/abs/2511.20015","url_pdf":"https://arxiv.org/pdf/2511.20015v1","authors":"[\"Xiucheng Wang\",\"Tingwei Yuan\",\"Yang Cao\",\"Nan Cheng\",\"Ruijin Sun\",\"Weihua Zhuang\"]","published":"2025-11-25T07:32:49Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"eess.SY\"]","methods":"[\"Diffusion Model\"]","has_code":false,"code_links":[{"ID":606629,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2836801,"paper_url":"https://arxiv.org/abs/2511.20015","paper_title":"iRadioDiff: Physics-Informed Diffusion Model for Indoor Radio Map Construction and Localization","repo_url":"https://github.com/UNIC-Lab/iRadioDiff","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}