This article presents a novel methodology for predicting wireless signal propagation using ray-tracing algorithms, and visualizing signal variations in situ by leveraging Augmented Reality (AR) tools. The proposed system performs a special type of spatial mapping, capable of converting a scanned indoor environment to a vector facet model. A ray-tracing algorithm uses the facet model for wireless signal predictions. Finally, an AR application overlays the signal strength predictions on the physical space in the form of holograms. Although some indoor reconstruction models have already been developed, this paper proposes an image to a facet algorithm for indoor reconstruction and compares its performance with existing AR algorithms, such as spatial understanding that are modified to create the required facet models. In addition, the paper orchestrates AR and ray-tracing techniques to provide an in situ network visualization interface. It is shown that the accuracy of the derived facet models is acceptable, and the overall signal predictions are not significantly affected by any potential inaccuracies of the indoor reconstruction. With the expected increase of densely deployed indoor 5G networks, it is believed that these types of AR applications for network visualization will play a key role in the successful planning of 5G networks.

中文翻译：

---

使用增强现实和光线跟踪的原位无线通道可视化。

本文介绍了一种新颖的方法，可使用光线跟踪算法预测无线信号的传播，并利用增强现实（AR）工具在现场可视化信号变化。拟议的系统执行一种特殊类型的空间映射，能够将扫描的室内环境转换为矢量构面模型。光线跟踪算法将构面模型用于无线信号预测。最后，AR应用程序将信号强度预测以全息图的形式叠加在物理空间上。尽管已经开发了一些室内重建模型，但本文提出了一种用于分面算法的图像用于室内重建，并将其性能与现有的AR算法进行了比较，例如对空间的理解进行了修改，以创建所需的分面模型。此外，该论文精心编排了AR和光线跟踪技术，以提供现场网络可视化界面。结果表明，导出的构面模型的准确性是可以接受的，并且室内重建的任何潜在误差都不会显着影响总体信号预测。预计随着密集部署的室内5G网络的增加，这些用于网络可视化的AR应用程序类型将在成功规划5G网络中发挥关键作用。