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Air–Ground Channel Characterization for Unmanned Aircraft Systems—Part I: Methods, Measurements, and Models for Over-Water Settings
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Abstract
The use of unmanned aerial systems (UASs), which are also known as unmanned aerial vehicles, and by the term “drones” in the popular press, is growing rapidly. To ensure safety, UAS control and nonpayload communication (CNPC) links must operate very reliably in a variety of conditions. This requires an accurate quantitative characterization of the air-ground (AG) channel, and this channel characterization is the focus of this paper. After providing motivation and background, we describe our methods and modeling approach, followed by a description of our simultaneous dual-band (L-band ~970 MHz, C-band ~5 GHz) measurement campaign and the over-water (OW) measurement sites. Example results for path loss and root-mean-square delay spread are provided, as well as the results for channel stationarity distance (SD), used in calculating small-scale Rician K- factor and correlations between the two receiver antennas that we employed in each frequency band. Two distinct SD measures-the power delay profile (PDP) correlation coefficient and the spatial autocorrelation matrix collinearity-were used and found to be of the same order. Path-loss exponents are near that of free space, but significant two-ray cancelation effects for these OW settings warrant more accurate models, which we provide. Delay spreads in the OW channels are also dominated by the two-ray components and are hence typically very small (~10 ns) but can exceed 350 ns. A third intermittent multipath component (MPC) is also present a nonnegligible fraction of time; hence, we provide statistical wideband AG channel models to represent this. Future papers in this series will report results for the AG channel with ground sites in other types of environments.
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Cite it as
@article{matolak2016airground,
title = {Air–Ground Channel Characterization for Unmanned Aircraft Systems—Part I: Methods, Measurements, and Models for Over-Water Settings},
author = {David W. Matolak and Ruoyu Sun},
year = {2016},
journal = {IEEE Transactions on Vehicular Technology},
publisher = {Institute of Electrical and Electronics Engineers},
volume = {66},
number = {1},
pages = {26--44},
doi = {10.1109/tvt.2016.2530306},
}