A three‐dimensional two‐hemisphere model for unmanned aerial vehicle multiple‐input multiple‐output channels

Abstract

The application of unmanned aerial vehicles (UAVs) has recently attracted considerable interest in various areas. A three‐dimensional multiple‐input multiple‐output concentric two‐hemisphere model is proposed to characterize the scattering environment around a vehicle in an urban UAV‐to‐vehicle communication scenario. Multipath components of the model consisted of line‐of‐sight and single‐bounced components. This study focused on the key parameters that determine the scatterer distribution. A time‐variant process was used to analyze the nonstationarity of the proposed model. Vital statistical properties, such as the space–time–frequency correlation function, Doppler power spectral density, level‐crossing rate, average fade duration, and channel capacity, were derived and analyzed. The results indicated that with an increase in the maximum scatter radius, the time correlation and level‐crossing rate decreased, the frequency correlation function had a faster downward trend, and average fade duration increased. In addition, with the increase of concentration parameter, the time correlation, space correlation, and level‐crossing rate increased, average fade duration decreased, and Doppler power spectral density became flatter. The proposed model was compared with current geometry‐based stochastic models (GBSMs) and showed good consistency. In addition, we verified the nonstationarity in the temporal and spatial domains of the proposed model. These conclusions can be used as references in the design of more reasonable communication systems.

Special issue on autonomous unmanned aerial/ground vehicles and their applications

“…However, they have limitations for applications like establishing high‐speed wireless networks. In [4], a 3D geometry‐based stochastic model for UAV multiple‐input multiple‐output (MIMO) channels is presented. The UAV flying direction and location have a significant impact on MIMO performance.…”

“…For this special issue, we selected 11 key studies on (1) communication, networks, and mobility [1][2][3][4][5] and (2) object detection and tracking in autonomous driving [6][7][8][9][10][11].…”