UAV-aided wireless powered communication networks: Trajectory optimization and resource allocation for minimum throughput maximization

This paper investigates wireless powered communication network (WPCN) systems aided by unmanned aerial vehicle (UAV) where a UAV-mounted access point (AP) serves multiple energy-constrained ground terminals (GTs). Specifically, the UAVs first transmit the wireless energy transfer (WET) signals to charge the GTs in the downlink. Then, by utilizing the harvested energy, the GTs send their wireless information transmission (WIT) signals to the UAVs in the uplink. In this paper, depending on the operations of the UAVs, we consider two different scenarios, namely integrated and separated UAV WPCNs. First, in the integrated system, a UAV acts as a hybrid AP in which both energy transfer and information reception are performed at a single UAV. In contrast, for the separated UAV WPCN, we consider two UAVs each of which behaves as an information AP and an energy AP independently, and thus the information decoding and the energy transfer are separately processed at two different UAVs. In each system, we formulate two optimization problems taking into account a linear energy harvesting (EH) model and a practical non-linear model. To maximize the minimum throughput of the GTs, we jointly optimize the trajectories of the UAVs, the uplink power control, and the time resource allocation for the WET and the WIT. Since the formulated problems are non-convex, in the linear EH model-based system, we apply the concave-convex procedure by deriving appropriate convex bounds for non-convex constraints and identify the suboptimal solution for the problem by a proposed iterative algorithm. In the non-linear model-based system, we propose another algorithm to obtain an efficient solution by adopting the successive convex approximation method with the alternating optimization framework. Simulation results demonstrate the efficiency and the performance of the proposed algorithms compared to conventional schemes.