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Intelligent reflecting surfaces aided millimetre wave blockage prediction for vehicular communication

The ability of millimeter-wave (mmWave) to deliver gigabit throughput has led to its widespread adoption in Fifth Generation (5G) networks. However, mmWave links between Base Station (BS) and users can be easily obstructed by obstacles. In vehicular networks with dynamic environments and mobile user...

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Main Authors: Woon, Fu Seong, Leow, Chee Yen
Format: Conference or Workshop Item
Published: 2022
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/98796/
http://dx.doi.org/10.1109/ISTT56288.2022.9966540
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spelling my.utm.987962023-02-02T08:56:51Z http://eprints.utm.my/id/eprint/98796/ Intelligent reflecting surfaces aided millimetre wave blockage prediction for vehicular communication Woon, Fu Seong Leow, Chee Yen TK Electrical engineering. Electronics Nuclear engineering The ability of millimeter-wave (mmWave) to deliver gigabit throughput has led to its widespread adoption in Fifth Generation (5G) networks. However, mmWave links between Base Station (BS) and users can be easily obstructed by obstacles. In vehicular networks with dynamic environments and mobile users, the mmWave link blockage issue is even more pronounced. In order to preserve the mmWave link in the vehicular network, it is necessary to predict blockages. For blockage prediction, sensor information from Lidar, Radar, and cameras has been considered. Nonetheless, these non-radio frequency methods necessitate the use of additional equipment and signal processing, which raises the implementation cost and complexity. The existing literature also considers the use of BS and user's Radio Frequency (RF) signatures to predict blockage. However, users' mobility has not been taken into account. An Intelligent Reflecting Surface (IRS), on the other hand, has been viewed as a promising method for providing an alternate path by reflecting the mmWave signal between the BS and user in order to improve the reliability of vehicular networks. Therefore, this research investigates the IRS-assisted blockage prediction in order to determine the future link status in the vehicular environment with respect to user mobility. The proposed solution employs a number of active elements that are randomly distributed on the IRS to obtain the RF signatures. Furthermore, it utilises Machine Learning (ML) techniques to learn the pre-blockage wireless signatures, which can predict future blockages. The results indicate that the proposed method can predict blockages between a single IRS and a moving user with a greater than 98 percent accuracy up to one second before they occur. 2022 Conference or Workshop Item PeerReviewed Woon, Fu Seong and Leow, Chee Yen (2022) Intelligent reflecting surfaces aided millimetre wave blockage prediction for vehicular communication. In: 2022 6th IEEE International Symposium on Telecommunication Technologies, ISTT 2022, 14 November - 16 November 2022, Johor Bahru, Malaysia. http://dx.doi.org/10.1109/ISTT56288.2022.9966540
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Woon, Fu Seong
Leow, Chee Yen
Intelligent reflecting surfaces aided millimetre wave blockage prediction for vehicular communication
description The ability of millimeter-wave (mmWave) to deliver gigabit throughput has led to its widespread adoption in Fifth Generation (5G) networks. However, mmWave links between Base Station (BS) and users can be easily obstructed by obstacles. In vehicular networks with dynamic environments and mobile users, the mmWave link blockage issue is even more pronounced. In order to preserve the mmWave link in the vehicular network, it is necessary to predict blockages. For blockage prediction, sensor information from Lidar, Radar, and cameras has been considered. Nonetheless, these non-radio frequency methods necessitate the use of additional equipment and signal processing, which raises the implementation cost and complexity. The existing literature also considers the use of BS and user's Radio Frequency (RF) signatures to predict blockage. However, users' mobility has not been taken into account. An Intelligent Reflecting Surface (IRS), on the other hand, has been viewed as a promising method for providing an alternate path by reflecting the mmWave signal between the BS and user in order to improve the reliability of vehicular networks. Therefore, this research investigates the IRS-assisted blockage prediction in order to determine the future link status in the vehicular environment with respect to user mobility. The proposed solution employs a number of active elements that are randomly distributed on the IRS to obtain the RF signatures. Furthermore, it utilises Machine Learning (ML) techniques to learn the pre-blockage wireless signatures, which can predict future blockages. The results indicate that the proposed method can predict blockages between a single IRS and a moving user with a greater than 98 percent accuracy up to one second before they occur.
format Conference or Workshop Item
author Woon, Fu Seong
Leow, Chee Yen
author_facet Woon, Fu Seong
Leow, Chee Yen
author_sort Woon, Fu Seong
title Intelligent reflecting surfaces aided millimetre wave blockage prediction for vehicular communication
title_short Intelligent reflecting surfaces aided millimetre wave blockage prediction for vehicular communication
title_full Intelligent reflecting surfaces aided millimetre wave blockage prediction for vehicular communication
title_fullStr Intelligent reflecting surfaces aided millimetre wave blockage prediction for vehicular communication
title_full_unstemmed Intelligent reflecting surfaces aided millimetre wave blockage prediction for vehicular communication
title_sort intelligent reflecting surfaces aided millimetre wave blockage prediction for vehicular communication
publishDate 2022
url http://eprints.utm.my/id/eprint/98796/
http://dx.doi.org/10.1109/ISTT56288.2022.9966540
_version_ 1758578020757536768
score 13.211869

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