Title: Radiolunadiff: Estimation of wireless network signal strength in lunar terrain Show Chinese title

Title: Radiolunadiff：月球地形中无线网络信号强度的估计

Abstract: 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. Show Chinese abstract

Abstract: 在本文中，我们提出了一种新颖的物理信息深度学习架构，用于预测月球地形上的无线电图。 我们的方法结合了一个基于物理的月球地形生成器，该生成器利用公开的NASA数据生成现实的地形，与射线追踪引擎结合，创建了高保真的无线电传播场景数据集。 基于此数据集，我们引入了一个三元组-UNet架构，由两个标准UNet和一个扩散网络组成，以模拟复杂的传播效应。 实验结果表明，我们的方法在各种指标上优于现有深度学习方法在我们的地形数据集上的表现。