Computer vision assisted mmWave beamforming for UAV-to-vehicle links

Jiaqi ZOU; Yuanhao CUI; Zixuan ZOU; Yuyang LIU; Guanyu ZHANG; Songlin SUN; Weijie YUAN

doi:10.1145/3556562.3558565

Computer vision assisted mmWave beamforming for UAV-to-vehicle links

Jiaqi ZOU, Yuanhao CUI, Zixuan ZOU, Yuyang LIU, Guanyu ZHANG, Songlin SUN, Weijie YUAN

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

1 Citation (Scopus)

Abstract

This paper focuses on the beamforming algorithm for UAV-to-vehicle communications. To deal with high communication overhead caused by beam tracking in high mobility communication scenarios, we utilize the inherent vision functionality of UAV platforms and propose a vision-assisted beamforming framework. We propose to use a deep-learning-based network for vehicle detection. Based on the predicted positions of vehicles, we propose a lightweight beamforming algorithm to save beam tracking overhead. Experiments and simulations are implemented on the UAV detection and tracking (UAVDT) dataset, which shows that the proposed algorithm gains a significant performance on received signal-to-interference-plus-noise ratio (SINR).

Original language	English
Title of host publication	ISACom 2022 : Proceedings of the 2022 1st ACM MobiCom Workshop on Integrated Sensing and Communications Systems, Part of MobiCom 2022
Publisher	Association for Computing Machinery, Inc
Pages	7-11
Number of pages	5
ISBN (Electronic)	9781450395250
DOIs	https://doi.org/10.1145/3556562.3558565
Publication status	Published - 21 Oct 2022
Externally published	Yes
Event	1st ACM MobiCom Workshop on Integrated Sensing and Communications Systems, ISACom 2022 - Part of MobiCom 2022 - Sydney, Australia Duration: 17 Oct 2022 → 17 Oct 2022

Conference

Conference	1st ACM MobiCom Workshop on Integrated Sensing and Communications Systems, ISACom 2022 - Part of MobiCom 2022
Country/Territory	Australia
City	Sydney
Period	17/10/22 → 17/10/22

Bibliographical note

Publisher Copyright:
© 2022 ACM.

Keywords

Integrated sensing and communications
predictive beamforming
unmanned aerial vehicle

Access to Document

10.1145/3556562.3558565

Cite this

ZOU, J., CUI, Y., ZOU, Z., LIU, Y., ZHANG, G., SUN, S., & YUAN, W. (2022). Computer vision assisted mmWave beamforming for UAV-to-vehicle links. In ISACom 2022 : Proceedings of the 2022 1st ACM MobiCom Workshop on Integrated Sensing and Communications Systems, Part of MobiCom 2022 (pp. 7-11). Association for Computing Machinery, Inc. https://doi.org/10.1145/3556562.3558565

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title = "Computer vision assisted mmWave beamforming for UAV-to-vehicle links",

abstract = "This paper focuses on the beamforming algorithm for UAV-to-vehicle communications. To deal with high communication overhead caused by beam tracking in high mobility communication scenarios, we utilize the inherent vision functionality of UAV platforms and propose a vision-assisted beamforming framework. We propose to use a deep-learning-based network for vehicle detection. Based on the predicted positions of vehicles, we propose a lightweight beamforming algorithm to save beam tracking overhead. Experiments and simulations are implemented on the UAV detection and tracking (UAVDT) dataset, which shows that the proposed algorithm gains a significant performance on received signal-to-interference-plus-noise ratio (SINR).",

keywords = "Integrated sensing and communications, predictive beamforming, unmanned aerial vehicle",

author = "Jiaqi ZOU and Yuanhao CUI and Zixuan ZOU and Yuyang LIU and Guanyu ZHANG and Songlin SUN and Weijie YUAN",

note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 1st ACM MobiCom Workshop on Integrated Sensing and Communications Systems, ISACom 2022 - Part of MobiCom 2022 ; Conference date: 17-10-2022 Through 17-10-2022",

year = "2022",

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doi = "10.1145/3556562.3558565",

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pages = "7--11",

booktitle = "ISACom 2022 : Proceedings of the 2022 1st ACM MobiCom Workshop on Integrated Sensing and Communications Systems, Part of MobiCom 2022",

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}

ZOU, J, CUI, Y, ZOU, Z, LIU, Y, ZHANG, G, SUN, S & YUAN, W 2022, Computer vision assisted mmWave beamforming for UAV-to-vehicle links. in ISACom 2022 : Proceedings of the 2022 1st ACM MobiCom Workshop on Integrated Sensing and Communications Systems, Part of MobiCom 2022. Association for Computing Machinery, Inc, pp. 7-11, 1st ACM MobiCom Workshop on Integrated Sensing and Communications Systems, ISACom 2022 - Part of MobiCom 2022, Sydney, Australia, 17/10/22. https://doi.org/10.1145/3556562.3558565

Computer vision assisted mmWave beamforming for UAV-to-vehicle links. / ZOU, Jiaqi; CUI, Yuanhao; ZOU, Zixuan et al.
ISACom 2022 : Proceedings of the 2022 1st ACM MobiCom Workshop on Integrated Sensing and Communications Systems, Part of MobiCom 2022. Association for Computing Machinery, Inc, 2022. p. 7-11.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - Computer vision assisted mmWave beamforming for UAV-to-vehicle links

AU - ZOU, Jiaqi

AU - CUI, Yuanhao

AU - ZOU, Zixuan

AU - LIU, Yuyang

AU - ZHANG, Guanyu

AU - SUN, Songlin

AU - YUAN, Weijie

PY - 2022/10/21

Y1 - 2022/10/21

N2 - This paper focuses on the beamforming algorithm for UAV-to-vehicle communications. To deal with high communication overhead caused by beam tracking in high mobility communication scenarios, we utilize the inherent vision functionality of UAV platforms and propose a vision-assisted beamforming framework. We propose to use a deep-learning-based network for vehicle detection. Based on the predicted positions of vehicles, we propose a lightweight beamforming algorithm to save beam tracking overhead. Experiments and simulations are implemented on the UAV detection and tracking (UAVDT) dataset, which shows that the proposed algorithm gains a significant performance on received signal-to-interference-plus-noise ratio (SINR).

AB - This paper focuses on the beamforming algorithm for UAV-to-vehicle communications. To deal with high communication overhead caused by beam tracking in high mobility communication scenarios, we utilize the inherent vision functionality of UAV platforms and propose a vision-assisted beamforming framework. We propose to use a deep-learning-based network for vehicle detection. Based on the predicted positions of vehicles, we propose a lightweight beamforming algorithm to save beam tracking overhead. Experiments and simulations are implemented on the UAV detection and tracking (UAVDT) dataset, which shows that the proposed algorithm gains a significant performance on received signal-to-interference-plus-noise ratio (SINR).

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KW - predictive beamforming

KW - unmanned aerial vehicle

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M3 - Conference paper (refereed)

AN - SCOPUS:85140208714

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BT - ISACom 2022 : Proceedings of the 2022 1st ACM MobiCom Workshop on Integrated Sensing and Communications Systems, Part of MobiCom 2022

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T2 - 1st ACM MobiCom Workshop on Integrated Sensing and Communications Systems, ISACom 2022 - Part of MobiCom 2022

Y2 - 17 October 2022 through 17 October 2022

ER -

Computer vision assisted mmWave beamforming for UAV-to-vehicle links

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Keywords

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