用于无线场建模的物理展开神经算子
Physics-Unrolled Neural Operator for Wireless Field Modeling
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出物理展开混合神经算子（PU-HNO），通过三级级联结构逐步捕捉无线传播效应，在不同平面图上的实验中，其在图像质量和无线部署指标上均优于多种基线模型。
AI中文摘要：
无线电地图是无线决策任务（如接入点部署、覆盖规划和定位）的关键，但它们的精细空间细节受复杂传播效应支配，准确模拟的成本很高。机器学习为高保真无线电地图预测提供了途径，无需为每个场景运行昂贵的高保真模拟。然而，大规模生成高质量训练标签也很困难：可负担的标签来自有限射线模拟，这类标签比低保真输入更丰富，但带有残余蒙特卡罗噪声。我们通过物理展开混合神经算子（PU-HNO）解决这一挑战，该模型是一个三级级联结构，通过逐步捕捉反射、衍射和散射效应，从低保真射线追踪输出和场景先验预测高保真室内无线电地图，而非将无线电地图视为普通图像。我们证明，在条件无偏标签噪声下，该模型可学习稳定的传播结构，且性能优于自身训练标签。在不同平面图上开展的实验表明，PU-HNO在图像质量和无线部署指标上均优于图像到图像基线、无线学习模型及整体神经算子。
英文摘要：
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.
