场景条件PINN-GNN用于多径射频地图：跨场景生成与场景内补全
Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion
- Department of Electronic Engineering and Information Science, University of Science and Technology of China（电子工程与信息科学系，中国科学技术大学）
- Wireless Technology Lab, Huawei（华为无线技术实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出基于物理信息神经网络和图神经网络的统一射频地图构建框架，支持跨场景生成和场景内补全，采用峰值加权动态时间弯曲度量评估多径重建质量，实验表明优于多种基线方法。
AI中文摘要：
射频地图提供了多径传播特性的紧凑表示，是信道建模、覆盖分析和环境感知无线优化的基础。本文提出一个基于物理信息神经网络和图神经网络的统一射频地图构建框架，支持使用2D和2.5D环境表示的跨场景生成和场景内补全。PINN嵌入电磁传播约束，建立从接收位置到多径参数（包括路径增益、到达时间和角度）的物理一致映射，而GNN通过建模相邻接收机之间的相关性来强制执行空间一致性。为了全面评估多径重建质量，我们提出一个峰值加权动态时间弯曲度量，该度量共同考虑信道冲激响应中的幅度误差和峰值延迟错位。大量实验表明，所提方法在地图级和多径级指标上始终优于基于图像、基于扩散和插值的基线方法，在稀疏观测下实现了鲁棒的泛化和高保真射频地图构建。
英文摘要：
Radio frequency (RF) maps provide a compact representation of multipath propagation characteristics and are fundamental to channel modeling, coverage analysis, and environment-aware wireless optimization. This paper proposes a unified RF map construction framework based on a physics-informed neural network (PINN) and a graph neural network (GNN), supporting both cross-scene generation and in-scene completion with 2D and 2.5D environmental representations. The PINN embeds electromagnetic propagation constraints to establish a physically consistent mapping from receiver locations to multipath parameters, including path gain, time of arrival, and angles, while the GNN enforces spatial consistency by modeling correlations among neighboring receivers. To comprehensively evaluate multipath reconstruction quality, we propose a peak-weighted dynamic time warping metric that jointly accounts for amplitude errors and peak delay misalignment in channel impulse responses. Extensive experiments demonstrate that the proposed method consistently outperforms image-based, diffusion-based, and interpolation baselines across both map-level and multipath-level metrics, achieving robust generalization and high-fidelity RF map construction under sparse observations.
