用物理+图神经网络生成高保真无线信道图，支持跨场景预测和缺损补全。
Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion
- 融合电磁物理约束与空间邻近关系，构建双驱动信道图生成模型。
- 在稀疏采样下仍保持优异性能，峰值误差与时延错位均显著降低。
- 适合无线环境建模、覆盖优化等需要精准信道表征的场景。
射频（RF）地图为多径传播特性提供了紧凑表征，是信道建模、覆盖分析和环境感知无线优化的基础。本文提出一种基于物理信息神经网络（PINN）与图神经网络（GNN）的统一RF地图构建框架，支持基于2D和2.5D环境表示的跨场景生成与场景内补全。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、角度）的物理一致映射；GNN则通过建模相邻接收点间的相关性，强化空间一致性。为全面评估多径重建质量，提出一种峰值加权动态时间规整度量，联合考虑信道冲击响应中的幅度误差与峰值时延错位。大量实验表明，所提方法在地图级与多径级指标上均优于基于图像、扩散模型及插值的基线方法，在稀疏观测下实现鲁棒泛化与高保真构造。
原文摘要 · Abstract (English)
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.
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