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This paper introduces the Physics-Unrolled Hybrid Neural Operator (PU-HNO), a novel three-stage cascade model designed to generate high-fidelity indoor radio maps from low-fidelity ray-tracing outputs and scene priors. By specifically addressing the challenges posed by residual Monte Carlo noise in training labels, PU-HNO effectively captures complex propagation effects such as reflection, diffraction, and scattering. Experimental results demonstrate that PU-HNO not only surpasses traditional image-to-image baselines and wireless learning models but also achieves superior performance in both image quality and practical wireless deployment metrics.
PU-HNO outperforms traditional methods by learning stable propagation structures from noisy labels, revolutionizing indoor radio map generation.
Radio maps are essential for wireless decision-making tasks such as access-point placement, coverage planning, and localization, but their fine spatial details are governed by complex propagation effects and are costly to simulate accurately. Machine learning offers a path to high-fidelity radio-map prediction without running expensive high-fidelity simulations for every scene. However, generating high-quality training labels at scale is also difficult: the affordable labels come from finite-ray simulations, which are richer than low-fidelity inputs but carry residual Monte Carlo noise. We address this challenge with Physics-Unrolled Hybrid Neural Operator (PU-HNO), a three-stage cascade that predicts high-fidelity indoor radio maps from low-fidelity ray-tracing outputs and scene priors by progressively capturing reflection, diffraction, and scattering effects, rather than treating radio maps as generic images. We prove that, under conditionally unbiased label noise, the model can learn stable propagation structure and outperform its own training labels. Experiments across diverse floorplans show that PU-HNO outperforms image-to-image baselines, wireless learning models, and monolithic neural operators across both image-quality and wireless deployment metrics.
