Optimal 1D Trajectory Design for UAV-Enabled Multiuser Wireless Power Transfer

• Published in: IEEE transactions on communications Vol. 67; no. 8; pp. 5674 - 5688
• Main Authors: Hu, Yulin, Yuan, Xiaopeng, Xu, Jie, Schmeink, Anke
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Complexity; Complexity theory; Constraints; energy fairness; Equivalence; Hovering; Network topology; Nodes; Optimization; successive hover-and-fly; Topology; Trajectories; Trajectory; trajectory design; Unmanned aerial vehicle; Unmanned aerial vehicles; Wireless communication; Wireless networks; wireless power transfer; Wireless power transmission; Wireless sensor networks
• DOI: 10.1109/TCOMM.2019.2911294
• ISSN: 0090-6778

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless power transfer network, where a UAV flies at a constant altitude in the sky to provide wireless energy supply for a set of ground nodes with a linear topology. Our objective is to maximize the minimum received energy among all ground nodes by optimizing the UAV's one-dimensional (1D) trajectory, subject to the maximum UAV flying speed constraint. Different from previous works that only provided heuristic and locally optimal solutions, this paper is the first to present the globally optimal 1D UAV trajectory solution to the considered min-energy maximization problem. Toward this end, we first show that for any given speed-constrained UAV trajectory, we can always construct a maximum-speed trajectory and a speed-free trajectory, such that their combination can achieve the same received energy at all these ground nodes. Next, we transform the UAV-speed-constrained trajectory design problem into an equivalent UAV-speed-free problem, which is then optimally solved via the Lagrange dual method. The optimal 1D UAV trajectory solution follows the so-called successive hover-and-fly structure, i.e., the UAV successively hovers at a finite number of hovering points each for an optimized hovering duration, and flies among these hovering points at the maximum speed. Building upon the optimal UAV trajectory structure, we further present a low-complexity UAV trajectory design by first transforming the original problem into an equivalent non-convex problem with only the UAV hovering locations and durations as optimization variables and then updating the trajectory via the successive convex approximation technique. Our analysis shows that the low-complexity design is guaranteed to converge to a suboptimal solution at a significantly lower complexity irrespective of the geographical network size. Numerical results show that the proposed low-complexity design actually achieves the same performance as the proposed optimal solution, and both of them outperform the benchmark algorithms in prior works under different scenarios.