In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing
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 ind...
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doaj-a979f167713848e284c57121c21629582020-11-25T02:06:21ZengMDPI AGSensors1424-82202020-01-0120369010.3390/s20030690s20030690In Situ Wireless Channel Visualization Using Augmented Reality and Ray TracingGeorge Koutitas0Varun Kumar Siddaraju1Vangelis Metsis2Electrical Engineering, Texas State University, San Marcos, TX 78666, USAElectrical Engineering, Texas State University, San Marcos, TX 78666, USAComputer Science, Texas State University, San Marcos, TX 78666, USAThis 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.https://www.mdpi.com/1424-8220/20/3/6905g networksaugmented realityray tracingspatial mappingnetwork signal visualization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
George Koutitas Varun Kumar Siddaraju Vangelis Metsis |
spellingShingle |
George Koutitas Varun Kumar Siddaraju Vangelis Metsis In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing Sensors 5g networks augmented reality ray tracing spatial mapping network signal visualization |
author_facet |
George Koutitas Varun Kumar Siddaraju Vangelis Metsis |
author_sort |
George Koutitas |
title |
In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing |
title_short |
In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing |
title_full |
In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing |
title_fullStr |
In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing |
title_full_unstemmed |
In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing |
title_sort |
in situ wireless channel visualization using augmented reality and ray tracing |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-01-01 |
description |
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. |
topic |
5g networks augmented reality ray tracing spatial mapping network signal visualization |
url |
https://www.mdpi.com/1424-8220/20/3/690 |
work_keys_str_mv |
AT georgekoutitas insituwirelesschannelvisualizationusingaugmentedrealityandraytracing AT varunkumarsiddaraju insituwirelesschannelvisualizationusingaugmentedrealityandraytracing AT vangelismetsis insituwirelesschannelvisualizationusingaugmentedrealityandraytracing |
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