Online Trajectory and Resource Optimization for Stochastic UAV-Enabled MEC Systems

• Published in: IEEE transactions on wireless communications Vol. 21; no. 7; pp. 5629 - 5643
• Main Authors: Yang, Zheyuan, Bi, Suzhi, Zhang, Ying-Jun Angela
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous aerial vehicles; Complexity; Computational modeling; Design optimization; Edge computing; Energy consumption; Lyapunov optimization; Mobile computing; mobile edge computing; online algorithm; Optimization; Queues; Resource allocation; Resource management; Stability; stochastic data arrivals; Task analysis; Trajectory; Unmanned aerial vehicle (UAV); Unmanned aerial vehicles; user mobility; Wireless communication
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2022.3142365

The recent development of unmanned aerial vehicle (UAV) and mobile edge computing (MEC) technologies provides flexible and resilient computation services to mobile users out of the terrestrial computing service coverage. In this paper, we consider a UAV-enabled MEC platform that serves multiple mobile ground users with random movements and task arrivals. We aim to minimize the average weighted energy consumption of all users subject to the average UAV energy consumption and data queue stability constraints. We formulate the problem as a multi-stage stochastic optimization, and adopt Lyapunov optimization to convert it into per-slot deterministic problems with fewer optimizing variables. We design two reduced-complexity methods that solve the resource allocation and the UAV movement either in two sequential steps or jointly in one step. Both methods can guarantee to satisfy the average UAV energy and queue stability constraints, meanwhile achieving a tradeoff between the user energy consumption and the length of queue backlog. Simulation results show that the two methods significantly outperform the other benchmark methods including a learning-based method in reducing the energy consumption of ground users. In between, the proposed joint optimization method achieves better performance than the two-stage method at the cost of higher computational complexity.