Backhaul-Aware UAV-Aided Capacity Enhancement in Mixed FSO-RF Network

Future networks are expected to make substantial use of unmanned aerial vehicles (UAVs) as aerial base stations (BSs). The backhauling of UAVs is often considered with license-free and highbandwidth free-space optical (FSO) communication. Employing UAVs and FSO technology together is appropriate for numerous applications such as user offloading, network capacity enhancement, and relaying services. However, the reliability of the backhaul FSO link can be jeopardized by infrequent adverse weather conditions such as fog. In this study, we proposed the capacity enhancement of a ground BS (GBS) with the aid of an FSO-backhualed UAV aerial BS. In particular, we optimize the UAV’s circular trajectory and parameters (i.e., coverage radius and beamwidth) to maximize the total network throughput during both normal and adverse weather (e.g., fog events). Two trajectories, namely rate maximization (RMT) and fairness-constrained rate maximization (FRMT), are considered. A novel expression for the average capacity of the FSO backhaul over the entire trajectory is derived. The formulated problem aims to maximize the average network throughput with constraints pertaining to backhaul capacity, network fairness, and UAV parameters. It is shown that the UAV changes its trajectory using its coverage radius and directional antenna beamwidth according to the weather conditions and fairness requirements to maximize the total system capacity. Furthermore, real weather data from the cities of Edinburgh and London in the U.K. is used to evaluate the performance of the system under low-visibility conditions. The numerical results show the proposed FSO-backhauled UAV can provide significant capacity enhancement even in thin, light, and moderately foggy conditions.