UAV-based Mobile Wireless Power Transfer Systems with Joint Optimization of User Scheduling and Trajectory

Abstract

This paper investigates a mobile wireless power transfer (WPT) system by employing unmanned aerial vehicle (UAV) as mobile energy transmitter (ET) platform, which delivers wireless energy to multiple sensor nodes (SNs) equipped with energy receivers (ERs) on the ground. Intuitively, the aerial ET can adjust its locations freely to facilitate the energy supplement for the battery-constraint SNs located in arbitrary area. However, the flying trajectory of UAV directly affects the distance between itself and each SN during a finite charging period, which consequently has significant impacts on the attenuation amounts of the radio frequency (RF) signal as well as the charging efficiency. Furthermore, different scheduling schemes for each SN also plays an important role in the effectiveness of power transfer when UAV is located in different positions. Thus, how to optimally explore UAV’s mobility via trajectory design in combination with the proper scheduling stratagem is of crucial importance for maximizing the amount of energy transferred to all SNs during a finite time of flight. Then, our objective is to jointly optimize the UAV’s trajectory and SNs’ scheduling scheme under UAV’s flying constraints from two different perspectives, i.e. the maximization of sum harvested energy of all SNs and the maximization of the minimum received energy among all SNs, in order to satisfy the specific requirements of power transfer. Obviously, the former emphasizes on maximizing the effectiveness of the whole power transfer system, but the latter focuses on the fairness among all SNs. Whereas, the established two problems are all in non-convex mixed integer forms, which are quite challenging to solve. Therefore, we first decompose the original problem into two subproblems and then develop an efficient iterative algorithm by using the successive convex optimization technique, which leads to a suboptimal solution. Finally, numerical results are given to compare the differences for both the two proposed schemes and justify the performance gain of the proposed schemes as compared to other benchmark schemes.