research-article

SABRE: Swarm-Based Aerial Beamforming Radios: Experimentation and Emulation

Authors:

Subhramoy Mohanti,

Carlos Bocanegra,

Sara Garcia Sanchez,

Kaushik Roy ChowdhuryAuthors Info & Claims

IEEE Transactions on Wireless Communications, Volume 21, Issue 9

Pages 7460 - 7475

https://doi.org/10.1109/TWC.2022.3158866

Published: 01 September 2022 Publication History

Abstract

We propose a novel distributed beamforming framework for UAVs, called SABRE, wherein airborne transmitters synchronize their operations for data communication with target receivers. SABRE chooses the best-suited subset of transmitters that maximizes user-defined QoS, considering relative distances from receivers, traffic characteristics, cumulative SNR desired at the receiver, and individual SNR estimated for each link. This paper makes three main contributions: (i) It shows how to achieve distributed beamforming in challenging, aerial hovering conditions by accurately synchronizing start-times and eliminating relative clock offsets. (ii) It proposes an algorithm with polynomial complexity that groups transmitters and chooses the receiver, maximizing the number of satisfied receivers in each round. (iii) It experimentally validates the concept of aerial beamforming in a testbed composed of four DJI-M100 UAVs in realistic outdoor environments. We follow this up with at-scale emulation involving beamforming with multiple candidate UAV transmitters in Colosseum, the world’s largest RF emulator. SABRE keeps the overall network frame error rate below 10% with a probability of 0.95 and manifests a 40% improvement in meeting user QoS thresholds over classical resource allocation methods. From a community viewpoint, the beamforming code, UAV interfacing designs, and the Colosseum container will be released publicly, allowing further independent investigations.

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SABRE: Swarm-Based Aerial Beamforming Radios: Experimentation and Emulation

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cover image IEEE Transactions on Wireless Communications

IEEE Transactions on Wireless Communications Volume 21, Issue 9

Sept. 2022

1048 pages

ISSN:1536-1276

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1536-1276 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 September 2022

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Mohanti SRoy DEisen MCavalcanti DChowdhury K(2024)L-NORM: Learning and Network Orchestration at the Edge for Robot Connectivity and Mobility in Factory Floor EnvironmentsIEEE Transactions on Mobile Computing10.1109/TMC.2023.326664323:4(2898-2914)Online publication date: 1-Apr-2024
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