Online Optimization in UAV-Enabled MEC System: Minimizing Long-Term Energy Consumption Under Adapting to Heterogeneous Demands

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.

I. Introduction

As we enter beyond 5G networks and march into 6G, the Internet of Things (IoT) industry has also achieved substantial development, marking the beginning of the unprecedented surge growth of connected devices [1]. This evolution will undoubtedly bring significant challenges to future communication networks, especially in terms of frequency resources and network performance. Although densely deployed terrestrial base stations can mitigate the burden on the network to a certain extent, they are prone to overload or even failure when gathering large-scale users or occurring sudden disasters, which seriously affects the user’s Quality of Service (QoS). As a flying base station, due to its remarkable flexibility, mobility, and controllability, the unmanned aerial vehicle (UAV) has become an extremely promising solution for rapid deployment in areas with sudden environmental changes or lacking infrastructure [2]. Through rational and effective trajectory planning, the UAV can significantly decrease communication distances with users, improve channel quality, and lessen infrastructure costs. In addition, compared to remote cloud computing, mobile edge computing (MEC) can meet the stringent low latency and energy consumption requirements of users by sinking computing resources to the vicinity of users, and alleviate the pressure on the backhaul link of the core network [3]. Therefore, the UAV-enabled MEC networks, as a powerful paradigm, can assist users in processing computing tasks timely, improving their QoS.