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...

Full description

Bibliographic Details
Main Authors: George Koutitas, Varun Kumar Siddaraju, Vangelis Metsis
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/3/690
id doaj-a979f167713848e284c57121c2162958
record_format Article
spelling 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
_version_ 1724934445325090816