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原文摘要

Site-specific wireless design relies on ray tracing to predict how signal strength varies across a floorplan, but a converged indoor radio map takes tens of seconds to simulate and planning a single building requires thousands of them. A learned surrogate can remove most of that cost, yet training one runs into a second problem: high-fidelity labels are exactly what is too expensive to produce at scale, so the affordable labels come from finite-ray simulations that carry residual Monte Carlo noise. We address this with RayNO, a physics-unrolled neural operator that reconstructs the converged output of a ray tracer from one cheap low-fidelity run and scene priors. Rather than treating a radio map as a generic image, RayNO refines the field through three stages matched to reflection, diffraction, and scattering, each supervised by a label of matching physical complexity. We prove that when the label error is conditionally unbiased the learned operator can end up closer to the high-fidelity reference than the labels it was trained on, and we verify that condition directly in the simulator using repeated independent ray seeds. Across diverse floorplans, RayNO outperforms image-to-image baselines, wireless learning models, monolithic neural operators, and classical multi-fidelity regression on both image-quality and wireless deployment metrics, while reducing the cost of a high-fidelity map from roughly 28 s to 0.11 s.