Koutitas George, Kumar Siddaraju Varun, Metsis Vangelis
Electrical Engineering, Texas State University, TX 78666, USA.
Computer Science, Texas State University, TX 78666, USA.
Sensors (Basel). 2020 Jan 27;20(3):690. doi: 10.3390/s20030690.
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.
本文提出了一种使用光线追踪算法预测无线信号传播，并利用增强现实（AR）工具对信号变化进行现场可视化的新颖方法。所提出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小平面模型。光线追踪算法使用该小平面模型进行无线信号预测。最后，一个AR应用程序以全息图的形式将信号强度预测叠加在物理空间上。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室内重建的图像到小平面算法，并将其性能与现有的AR算法（如为创建所需小平面模型而修改的空间理解算法）进行比较。此外，本文将AR和光线追踪技术结合起来，提供一个现场网络可视化界面。结果表明，导出的小平面模型的准确性是可以接受的，并且整体信号预测不会受到室内重建中任何潜在不准确性的显著影响。随着密集部署的室内5G网络预计会增加，相信这些类型的用于网络可视化的AR应用将在5G网络的成功规划中发挥关键作用。
