UAV-Assisted MEC Networks With Aerial and Ground Cooperation

• Published in: IEEE transactions on wireless communications Vol. 20; no. 12; pp. 7712 - 7727
• Main Authors: Xu, Yu, Zhang, Tiankui, Liu, Yuanwei, Yang, Dingcheng, Xiao, Lin, Tao, Meixia
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computation efficiency; Computational efficiency; Computing time; Edge computing; Efficiency; Energy consumption; High altitude; Internet of Things; Maximization; Mobile computing; mobile edge computing; Network latency; Optimization; Resource management; Servers; Trajectory; trajectory optimization; unmanned aerial vehicle; Unmanned aerial vehicles; Wireless communication
• DOI: 10.1109/TWC.2021.3086521
• ISSN: 1536-1276

With the high altitude and flexible mobility, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) is becoming a promising technology to cope with the computation-intensive and latency-critical task in prospective Internet of Things. In this paper, we propose a novel MEC system with several ground servers at access points and one aerial server carried by UAV. To balance the vital metrics of the MEC system, computation bits and energy consumption, we aim to maximize the weighted computation efficiency of the system, subject to the constraints on communication and computation resources, minimum computation requirement and UAV's mobility. To this end, a joint optimization problem with the goal of weighted computation efficiency maximization is formulated. First, we analyze the problem and transform it into an equivalent tractable form. Then, we solve the challenging non-convex problem by jointly optimizing the computation task assignment, time slot partition, transmission bandwidth and CPU frequency allocation, transmit power allocation, and UAV's trajectory, based on the Dinkelbach's method, Lagrange duality and successive convex approximation technique. Furthermore, we propose an alternative computation efficiency maximization algorithm, followed by the convergence and complexity analysis. Finally, numerical simulations show that our proposed algorithm significantly improves the computation efficiency compared to benchmark schemes. It is also validated that the proposed algorithm effectively obtains a good tradeoff between the computation task bits and energy consumption of the system.