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 |
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| 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 | |
| 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 |
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Conference
| Conference | 16th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019 |
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| Country/Territory | United States |
| City | Monterey |
| Period | 4/11/19 → 7/11/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Funding
VI. CONCLUSION AirBeam achieves aerial beamforming communication under practical SWaP-C constraints. We have demonstrated the feasibility of a practical system with preliminary experiments and provided extensive systems level implementation on a real test bed. Comparing the performance of data communications with traditional aerial networks, we see that our approach achieves 30-40% reduction in BER, when the numbers of UAV transmitters are increased from 1 to 4 and 99% probability of BER to be near 0 with this approach. Our next steps will focus on demonstrating enhanced beamforming algorithms that can operate even with intermittent CSI information resulting from losses from the receiver-generated feedback packets. ACKNOWLEDGMENT This work is supported by DARPA under grant N66001-17-1-4042. We are grateful to Dr. Tom Rondeau, program manager at DARPA, for his insightful comments and suggestions that significantly improved the quality of the work.
| Funders | Funder number |
|---|---|
| Defense Advanced Research Projects Agency | N66001-17-1-4042 |
Keywords
- Distributed beamforming
- FPGA
- Software Defined Radio
- Unmanned Aerial Vehicle