用物理启发的神经算子，从低精度射线追踪生成高保真无线场图。
Physics-Unrolled Neural Operator for Wireless Field Modeling
- 分三阶段逐步建模反射、绕射和散射效应，融合物理规律与深度学习。
- 在多种户型测试中，性能超越训练标签本身，且优于主流图像与无线模型。
- 适合无线网络规划、定位等需要高精度场图的工程场景。
无线场图对接入点部署、覆盖规划和定位等任务至关重要，但其精细空间细节受复杂传播效应影响，高精度仿真成本高昂。机器学习可避免为每个场景运行昂贵仿真，实现高保真场图预测。然而，大规模生成高质量训练标签同样困难：低成本标签来自有限射线仿真，虽比低精度输入更丰富，但仍含蒙特卡洛噪声。本文提出物理解卷积混合神经算子（PU-HNO），一种三阶段级联框架，从低精度射线追踪输出和场景先验中逐步捕捉反射、绕射和散射效应，而非将场图视为普通图像。理论证明，在条件无偏噪声下，模型可学习稳定的传播结构，并超越自身训练标签。跨多种平面布局的实验表明，PU-HNO在图像质量与无线部署指标上均优于图像到图像基线、无线学习模型及单体神经算子。
原文摘要 · Abstract (English)
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.
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