iRadioDiff: Physics-Informed Diffusion Model for Indoor Radio Map Construction and Localization
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917102450900992 |
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| author | Wang, Xiucheng Yuan, Tingwei Cao, Yang Cheng, Nan Sun, Ruijin Zhuang, Weihua |
| author_facet | Wang, Xiucheng Yuan, Tingwei Cao, Yang Cheng, Nan Sun, Ruijin Zhuang, Weihua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20015 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | iRadioDiff: Physics-Informed Diffusion Model for Indoor Radio Map Construction and Localization Wang, Xiucheng Yuan, Tingwei Cao, Yang Cheng, Nan Sun, Ruijin Zhuang, Weihua Machine Learning Systems and Control 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. |
| title | iRadioDiff: Physics-Informed Diffusion Model for Indoor Radio Map Construction and Localization |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2511.20015 |