This paper studies a Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) system operating in the millimeter-wave (mmWave) band to address the energy efficiency demands of urban computation-intensive and delay-sensitive tasks. By exploiting mmWave’s large bandwidth and UAVs’ mobility, we propose a joint optimization framework for UAV deployment, task offloading, and resource allocation to minimize the system energy cost. Since the proposed problem is a mixed-integer nonlinear programming (MINLP) problem with high computational complexity, conventional solvers may fail to find efficient solutions. To address this, we propose an alternating optimization framework. Specifically, the original problem is decomposed into two subproblems: (1) UAV deployment combined with user offloading, and (2) resource allocation, which are solved iteratively. For the former, we employ an obstacle-aware K-means method integrated with coordinate search to optimize UAV positions and user offloading decisions. For the latter, we combine the Lambert W function and the Lagrangian dual method to efficiently allocate communication and computational resources. Simulation results demonstrate that the proposed method substantially lowers the system energy cost compared to baseline methods, presenting a practical solution for urban UAV-MEC systems.

Unmanned aerial vehicle (UAV) can work as a flying computing platform to supply computation services to users when the terrestrial infrastructure is insufficient or damaged, due to its high mobility, flexibility and controllability. However, there remain many challenges in practical UAV-assisted mobile edge computing (MEC) system. Among them, a unique challenge is how to coordinate communication and computing resources to adapt the diverse heterogeneous demands of users in dynamic network environments. Accordingly, this article investigates a more practical UAV-enabled MEC network, which considers the task backlog queues and the heterogeneous demands of users. With joint optimization transmit power, bandwidth ratio and UAV trajectory, we minimize the long-term energy consumption while ensuring the controllable task backlog queues. As the proposed problem is a long-term stochastic optimization problem, we utilize the Lyapunov method to transform it into two deterministic online optimization subproblems and iteratively solve them. Moreover, we design personalized Lyapunov control factors to meet the tradeoff between energy consumption and queue stability for different users with heterogeneous requirements. In terms of solving subproblems, for the first subproblem, we prove its convexity by using the convexity-preserving property of composite perspective function, and then obtain the closed-form optimal solution. For the second subproblem, we skillfully design a low-complexity trajectory scheduling algorithm by using successive convex approximation (SCA), penalty function, and convex function properties. The simulation results show that the proposed algorithm with a lower complexity effectively reduces the long-term energy consumption of the system while meeting the heterogeneous requirements of users.

This article investigates a wirelessly powered mobile edge computing (MEC) framework with the cooperation between an unmanned aerial vehicle (UAV) and a center Cloud. In this system, we minimize the waiting delay of user equipments (UEs) under controllable energy consumption for a UAV serving a large area, where the UAV is equipped with an MEC server and an energy transmitter (ET). Thus, it can provide energy and computing services for UEs and transmit intractable tasks to the Cloud for further execution. To make the UAV serve all UEs, we propose the method by using a long and short time slots mixed mechanism, and handle the offloading decision and flying area selection by a mixed strategy with the Stackelberg game architecture during long-time slots, while the resource allocation and trajectory optimization issues are settled within short time slots. Specifically, considering limited battery capacity and severe energy propagation loss of the wireless power transfer (WPT), energy consumption can be controlled through queue optimization in short time slots. We decompose the original nonconvex problem into a series of deterministic optimization subproblems based on the Lyapunov optimization theory. Furthermore, using the Lagrangian duality theory, alternative optimization, and successive convex approximation (SCA) technique, we can obtain the closed-form solutions to the original problem. Performance analysis and simulation results demonstrate the convergence and the effectiveness of our proposed algorithm.