LAUSR

In this paper, we investigate the resource allocation problem for unmanned aerial vehicle (UAV)-assisted networks, where a UAV acting as an energy source provides radio frequency energy for multiple energy harvesting-powered device-to-device (D2D) pairs with much information to be transmitted. The goal is to maximize the average throughput within a time horizon while satisfying the energy causality constraint under a generalized harvest-transmit-store model, which results in a non-convex problem. By introducing the Lagrangian relaxation method, we analytically show that the behavior of all D2D pairs at each time slot is exclusive: harvesting energy or transmitting information signals. The formulated non-convex optimization problem is thus transformed into a mixed integer nonlinear programming (MINIP). We then design an efficient resource allocation algorithm to solve this MINIP, where D.C. (difference of two convex functions) programming and golden section method are combined to achieve a suboptimal solution. Furthermore, we provide an idea to reduce the computational complexity for facilitating the application in practice. Simulations are conducted to validate the effectiveness of the proposed algorithm and evaluate the system throughput performance.