부산대학교 도서관

Unmanned aerial vehicle (UAV) mounted millimeter-wave (mmWave) base stations as well as aerial backhaul links will enable on-demand deployment of network resources. However, prior work has shown aerial links are prone to the frequent disruption caused by: 1) constant hovering due to GPS inaccuracies that impact narrow beamwidths; 2) blockages in the direct line of sight; and 3) suboptimal beam selection, especially if reduced angular sectors are searched in a highly dynamic environment. This article characterizes the impact of each of these phenomena for aerial mmWave links and proposes methods to distinctly identify when they occur in isolation or in combination during deployment. Furthermore, it also proposes corrective actions at the UAV, appropriate for the specific type(s) of impacting events: physical displacement from its earlier location, angular rotation around its vertical axis, or beamwidth adjustment. Our approach relies on exploiting the information contained in the angular domain of a large data set of experimentally collected beam-selection outcomes, under the above practical scenarios. We incorporate GPS accuracy models and antenna radiation patterns to create a robust model of potential outages. We then propose device-agnostic algorithms that jointly optimize UAVs’ physical movement and the beamforming procedure. The experimental results obtained by mounting a pair of 60-GHz channel sounders on M600 DJI UAVs reveal loss reduction of up to 74.7%, translated into 260% physical layer bit-rate improvement compared to the classical 802.11ad standards-defined approach.