4.7 Article

Millimeter-Wave Base Stations in the Sky: An Experimental Study of UAV-to-Ground Communications

Journal

IEEE TRANSACTIONS ON MOBILE COMPUTING
Volume 21, Issue 2, Pages 644-662

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/TMC.2020.3013575

Keywords

mmWave; hovering; UAV-to-Ground; channel model; beam selection

Funding

  1. Office of Naval Research [N000141612651]
  2. U.S. Department of Defense (DOD) [N000141612651] Funding Source: U.S. Department of Defense (DOD)

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This paper adopts a systems approach to study the high throughput links of millimeter wave (mmWave) radio transmitters on UAVs, and develops the first stochastic UAV-to-G round mmWave channel model with UAVs as transmitters.
This paper adopts a systems approach to study how millimeter wave (mmWave) radio transmitters on UAVs provide high throughput links under typical hovering conditions. With Terragraph channel sounder units, we experimentally study the impact of signal fluctuations and sub-optimal beam selection on a testbed involving DJI M600 UAVs. From the hovering-related insights and the measured antenna radiation patterns, we develop and validate the first stochastic UAV-to-G round mmWave channel model with UAVs as transmitters. Our UAV-centric analytical model complements the classical fading with additional losses expected in the mmWave channel during hovering, considering 3-D antenna configuration and beamforming training parameters. We specifically consider lateral displacement, roll, pitch, and yaw, whose magnitude vary depending on the availability of specialized hardware such as real-time kinematic GPS. We then leverage this model to mitigate the hovering impact on the UAV-to-Ground link by selecting a near-to-optimum pair of beams. Importantly, our work does not change the wireless standard nor require any cross-layer information, making it compatible with current mmWave devices. Results demonstrate that our channel model drops estimation error to 0.2 percent, i.e., 18x lower, and improves the average PHY bit-rate by 10 percent when compared to existing state-of-the-art channel models and beamforming methods for UAVs.

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