AirBeam: Experimental demonstration of distributed beamforming by a swarm of UAVs

Subhramoy Mohanti; Carlos Bocanegra; Jason Meyer; Gokhan Secinti; Mithun Diddi; Hanumant Singh; Kaushik Chowdhury

doi:10.1109/MASS.2019.00028

AirBeam: Experimental demonstration of distributed beamforming by a swarm of UAVs

Subhramoy Mohanti, Carlos Bocanegra, Jason Meyer, Gokhan Secinti, Mithun Diddi, Hanumant Singh, Kaushik Chowdhury

Department of Computer Engineering

Northeastern University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

27 Citations (Scopus)

Abstract

We propose AirBeam, the first complete algorithmic framework and systems implementation of distributed air-to-ground beamforming on a fleet of UAVs. AirBeam synchronizes software defined radios (SDRs) mounted on each UAV and assigns beamforming weights to ensure high levels of directivity. We show through an exhaustive set of the experimental studies on UAVs why this problem is difficult given the continuous hovering-related fluctuations, the need to ensure timely feedback from the ground receiver due to the channel coherence time, and the size, weight, power and cost (SWaP-C) constraints for UAVs. AirBeam addresses these challenges through: (i) a channel state estimation method using Gold sequences that is used for setting the suitable beamforming weights, (ii) adaptively starting transmission to synchronize the action of the distributed radios, (iii) a channel state feedback process that exploits statistical knowledge of hovering characteristics. Finally, AirBeam provides insights from a systems integration viewpoint, with reconfigurable B210 SDRs mounted on a fleet of DJI M100 UAVs, using GnuRadio running on an embedded computing host.

Original language	English
Title of host publication	Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	162-170
Number of pages	9
ISBN (Electronic)	9781728146010
DOIs	https://doi.org/10.1109/MASS.2019.00028
Publication status	Published - Nov 2019
Event	16th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019 - Monterey, United States Duration: 4 Nov 2019 → 7 Nov 2019

Publication series

Name	Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019

Conference

Conference	16th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019
Country/Territory	United States
City	Monterey
Period	4/11/19 → 7/11/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Distributed beamforming
FPGA
Software Defined Radio
Unmanned Aerial Vehicle

Access to Document

10.1109/MASS.2019.00028

Cite this

Mohanti, S., Bocanegra, C., Meyer, J., Secinti, G., Diddi, M., Singh, H., & Chowdhury, K. (2019). AirBeam: Experimental demonstration of distributed beamforming by a swarm of UAVs. In Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019 (pp. 162-170). Article 9077393 (Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MASS.2019.00028

Mohanti, Subhramoy ; Bocanegra, Carlos ; Meyer, Jason et al. / AirBeam : Experimental demonstration of distributed beamforming by a swarm of UAVs. Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 162-170 (Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019).

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abstract = "We propose AirBeam, the first complete algorithmic framework and systems implementation of distributed air-to-ground beamforming on a fleet of UAVs. AirBeam synchronizes software defined radios (SDRs) mounted on each UAV and assigns beamforming weights to ensure high levels of directivity. We show through an exhaustive set of the experimental studies on UAVs why this problem is difficult given the continuous hovering-related fluctuations, the need to ensure timely feedback from the ground receiver due to the channel coherence time, and the size, weight, power and cost (SWaP-C) constraints for UAVs. AirBeam addresses these challenges through: (i) a channel state estimation method using Gold sequences that is used for setting the suitable beamforming weights, (ii) adaptively starting transmission to synchronize the action of the distributed radios, (iii) a channel state feedback process that exploits statistical knowledge of hovering characteristics. Finally, AirBeam provides insights from a systems integration viewpoint, with reconfigurable B210 SDRs mounted on a fleet of DJI M100 UAVs, using GnuRadio running on an embedded computing host.",

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author = "Subhramoy Mohanti and Carlos Bocanegra and Jason Meyer and Gokhan Secinti and Mithun Diddi and Hanumant Singh and Kaushik Chowdhury",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019 ; Conference date: 04-11-2019 Through 07-11-2019",

year = "2019",

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doi = "10.1109/MASS.2019.00028",

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Mohanti, S, Bocanegra, C, Meyer, J, Secinti, G, Diddi, M, Singh, H & Chowdhury, K 2019, AirBeam: Experimental demonstration of distributed beamforming by a swarm of UAVs. in Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019., 9077393, Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019, Institute of Electrical and Electronics Engineers Inc., pp. 162-170, 16th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019, Monterey, United States, 4/11/19. https://doi.org/10.1109/MASS.2019.00028

AirBeam: Experimental demonstration of distributed beamforming by a swarm of UAVs. / Mohanti, Subhramoy; Bocanegra, Carlos; Meyer, Jason et al.
Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 162-170 9077393 (Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Mohanti, Subhramoy

AU - Bocanegra, Carlos

AU - Meyer, Jason

AU - Secinti, Gokhan

AU - Diddi, Mithun

AU - Singh, Hanumant

AU - Chowdhury, Kaushik

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N2 - We propose AirBeam, the first complete algorithmic framework and systems implementation of distributed air-to-ground beamforming on a fleet of UAVs. AirBeam synchronizes software defined radios (SDRs) mounted on each UAV and assigns beamforming weights to ensure high levels of directivity. We show through an exhaustive set of the experimental studies on UAVs why this problem is difficult given the continuous hovering-related fluctuations, the need to ensure timely feedback from the ground receiver due to the channel coherence time, and the size, weight, power and cost (SWaP-C) constraints for UAVs. AirBeam addresses these challenges through: (i) a channel state estimation method using Gold sequences that is used for setting the suitable beamforming weights, (ii) adaptively starting transmission to synchronize the action of the distributed radios, (iii) a channel state feedback process that exploits statistical knowledge of hovering characteristics. Finally, AirBeam provides insights from a systems integration viewpoint, with reconfigurable B210 SDRs mounted on a fleet of DJI M100 UAVs, using GnuRadio running on an embedded computing host.

AB - We propose AirBeam, the first complete algorithmic framework and systems implementation of distributed air-to-ground beamforming on a fleet of UAVs. AirBeam synchronizes software defined radios (SDRs) mounted on each UAV and assigns beamforming weights to ensure high levels of directivity. We show through an exhaustive set of the experimental studies on UAVs why this problem is difficult given the continuous hovering-related fluctuations, the need to ensure timely feedback from the ground receiver due to the channel coherence time, and the size, weight, power and cost (SWaP-C) constraints for UAVs. AirBeam addresses these challenges through: (i) a channel state estimation method using Gold sequences that is used for setting the suitable beamforming weights, (ii) adaptively starting transmission to synchronize the action of the distributed radios, (iii) a channel state feedback process that exploits statistical knowledge of hovering characteristics. Finally, AirBeam provides insights from a systems integration viewpoint, with reconfigurable B210 SDRs mounted on a fleet of DJI M100 UAVs, using GnuRadio running on an embedded computing host.

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PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 4 November 2019 through 7 November 2019

ER -

Mohanti S, Bocanegra C, Meyer J, Secinti G, Diddi M, Singh H et al. AirBeam: Experimental demonstration of distributed beamforming by a swarm of UAVs. In Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 162-170. 9077393. (Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019). doi: 10.1109/MASS.2019.00028

AirBeam: Experimental demonstration of distributed beamforming by a swarm of UAVs

Abstract

Publication series

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Bibliographical note

Keywords

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