Beam alignment for millimetre wave links with motion prediction of autonomous vehicles

Ioannis Mavromatis; Andrea Tassi; Robert J. Piechocki; Andrew Nix

Beam alignment for millimetre wave links with motion prediction of autonomous vehicles

Ioannis Mavromatis, Andrea Tassi, Robert J. Piechocki, Andrew Nix

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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Abstract

Intelligent Transportation Systems (ITSs) require ultra-low end-to-end delays and multi-gigabit-per-second data transmission. Millimetre Waves (mmWaves) communications can fulfil these requirements. However, the increased mobility of Connected and Autonomous Vehicles (CAVs), requires frequent beamforming - thus introducing increased overhead. In this paper, a new beamforming algorithm is proposed able to achieve overhead-free beamforming training. Leveraging from the CAVs sensory data, broadcast with Dedicated Short Range Communications (DSRC) beacons, the position and the motion of a CAV can be estimated and beamform accordingly. To minimise the position errors, an analysis of the distinct error components was presented. The network performance is further enhanced by adapting the antenna beamwidth with respect to the position error. Our algorithm outperforms the legacy IEEE 802.11ad approach proving it a viable solution for the future ITS applications and services.

Original language	English
Title of host publication	Colloquium on Antennas, Propagation & RF Technology for Transport and Autonomous Platforms 201
Subtitle of host publication	Proceedings of a meeting held 2 February 2017, Birmingham, UK
Publisher	Institution of Engineering and Technology (IET)
Pages	29-36
Number of pages	8
ISBN (Print)	9781510861404
Publication status	Published - 1 May 2018

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Mavromatis, I., Tassi, A., Piechocki, R. J., & Nix, A. (2018). Beam alignment for millimetre wave links with motion prediction of autonomous vehicles. In Colloquium on Antennas, Propagation & RF Technology for Transport and Autonomous Platforms 201 : Proceedings of a meeting held 2 February 2017, Birmingham, UK (pp. 29-36). Institution of Engineering and Technology (IET).

Mavromatis, Ioannis ; Tassi, Andrea ; Piechocki, Robert J. et al. / Beam alignment for millimetre wave links with motion prediction of autonomous vehicles. Colloquium on Antennas, Propagation & RF Technology for Transport and Autonomous Platforms 201 : Proceedings of a meeting held 2 February 2017, Birmingham, UK. Institution of Engineering and Technology (IET), 2018. pp. 29-36

@inproceedings{86cda36a91e84b91b973ae766e06a59b,

title = "Beam alignment for millimetre wave links with motion prediction of autonomous vehicles",

abstract = "Intelligent Transportation Systems (ITSs) require ultra-low end-to-end delays and multi-gigabit-per-second data transmission. Millimetre Waves (mmWaves) communications can fulfil these requirements. However, the increased mobility of Connected and Autonomous Vehicles (CAVs), requires frequent beamforming - thus introducing increased overhead. In this paper, a new beamforming algorithm is proposed able to achieve overhead-free beamforming training. Leveraging from the CAVs sensory data, broadcast with Dedicated Short Range Communications (DSRC) beacons, the position and the motion of a CAV can be estimated and beamform accordingly. To minimise the position errors, an analysis of the distinct error components was presented. The network performance is further enhanced by adapting the antenna beamwidth with respect to the position error. Our algorithm outperforms the legacy IEEE 802.11ad approach proving it a viable solution for the future ITS applications and services.",

author = "Ioannis Mavromatis and Andrea Tassi and Piechocki, {Robert J.} and Andrew Nix",

year = "2018",

month = may,

day = "1",

language = "English",

isbn = "9781510861404",

pages = "29--36",

booktitle = "Colloquium on Antennas, Propagation & RF Technology for Transport and Autonomous Platforms 201",

publisher = "Institution of Engineering and Technology (IET)",

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Beam alignment for millimetre wave links with motion prediction of autonomous vehicles. / Mavromatis, Ioannis; Tassi, Andrea; Piechocki, Robert J. et al.

Colloquium on Antennas, Propagation & RF Technology for Transport and Autonomous Platforms 201 : Proceedings of a meeting held 2 February 2017, Birmingham, UK. Institution of Engineering and Technology (IET), 2018. p. 29-36.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Beam alignment for millimetre wave links with motion prediction of autonomous vehicles

AU - Mavromatis, Ioannis

AU - Tassi, Andrea

AU - Piechocki, Robert J.

AU - Nix, Andrew

PY - 2018/5/1

Y1 - 2018/5/1

N2 - Intelligent Transportation Systems (ITSs) require ultra-low end-to-end delays and multi-gigabit-per-second data transmission. Millimetre Waves (mmWaves) communications can fulfil these requirements. However, the increased mobility of Connected and Autonomous Vehicles (CAVs), requires frequent beamforming - thus introducing increased overhead. In this paper, a new beamforming algorithm is proposed able to achieve overhead-free beamforming training. Leveraging from the CAVs sensory data, broadcast with Dedicated Short Range Communications (DSRC) beacons, the position and the motion of a CAV can be estimated and beamform accordingly. To minimise the position errors, an analysis of the distinct error components was presented. The network performance is further enhanced by adapting the antenna beamwidth with respect to the position error. Our algorithm outperforms the legacy IEEE 802.11ad approach proving it a viable solution for the future ITS applications and services.

AB - Intelligent Transportation Systems (ITSs) require ultra-low end-to-end delays and multi-gigabit-per-second data transmission. Millimetre Waves (mmWaves) communications can fulfil these requirements. However, the increased mobility of Connected and Autonomous Vehicles (CAVs), requires frequent beamforming - thus introducing increased overhead. In this paper, a new beamforming algorithm is proposed able to achieve overhead-free beamforming training. Leveraging from the CAVs sensory data, broadcast with Dedicated Short Range Communications (DSRC) beacons, the position and the motion of a CAV can be estimated and beamform accordingly. To minimise the position errors, an analysis of the distinct error components was presented. The network performance is further enhanced by adapting the antenna beamwidth with respect to the position error. Our algorithm outperforms the legacy IEEE 802.11ad approach proving it a viable solution for the future ITS applications and services.

M3 - Conference Contribution (Conference Proceeding)

SN - 9781510861404

SP - 29

EP - 36

BT - Colloquium on Antennas, Propagation & RF Technology for Transport and Autonomous Platforms 201

PB - Institution of Engineering and Technology (IET)

ER -

Beam alignment for millimetre wave links with motion prediction of autonomous vehicles

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