用物理模型生成月球地形，预测无线信号强度分布。
Radiolunadiff: Estimation of wireless network signal strength in lunar terrain
- 结合月球地形生成与光线追踪，构建高保真信号数据集。
- 在多种指标上优于现有深度学习方法，精度显著提升。
- 适合研究月球通信与深空网络部署的科研人员。
本文提出一种新型物理信息深度学习架构，用于预测月球地形上的无线电信号强度分布。该方法融合基于公开NASA数据的物理驱动月球地形生成器与光线追踪引擎，构建了高保真无线电传播场景数据集。在此基础上，提出由两个标准UNet和一个扩散网络组成的三重UNet架构，以建模复杂的信号传播效应。实验结果表明，该方法在自建地形数据集上，于多项评估指标中均优于现有深度学习方法。
原文摘要 · Abstract (English)
In this paper, we propose a novel physics-informed deep learning architecture for predicting radio maps over lunar terrain. Our approach integrates a physics-based lunar terrain generator, which produces realistic topography informed by publicly available NASA data, with a ray-tracing engine to create a high-fidelity dataset of radio propagation scenarios. Building on this dataset, we introduce a triplet-UNet architecture, consisting of two standard UNets and a diffusion network, to model complex propagation effects. Experimental results demonstrate that our method outperforms existing deep learning approaches on our terrain dataset across various metrics.
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