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.

中文翻译：

---

无人机MEC系统在线优化：适应异构需求，最小化长期能耗


无人机以其高度的机动性、灵活性和可控性，可以作为飞行计算平台，在地面基础设施不足或损坏时为用户提供计算服务。然而，实用的无人机辅助移动边缘计算（MEC）系统仍然存在许多挑战。其中，一个独特的挑战是如何协调通信和计算资源以适应动态网络环境中用户多样化的异构需求。因此，本文研究了一种更实用的无人机MEC网络，该网络考虑了任务积压队列和用户的异构需求。通过联合优化发射功率、带宽比和无人机轨迹，在保证任务积压队列可控的同时，最小化长期能耗。由于所提出的问题是一个长期随机优化问题，我们利用Lyapunov方法将其转化为两个确定性在线优化子问题并迭代求解。此外，我们设计了个性化的Lyapunov控制因子，以满足具有异构需求的不同用户的能耗和队列稳定性之间的权衡。在求解子问题方面，对于第一个子问题，利用复合透视函数的保凸性证明其凸性，进而得到闭式最优解。对于第二个子问题，我们利用逐次凸逼近（SCA）、罚函数和凸函数性质巧妙地设计了一种低复杂度的轨迹调度算法。 仿真结果表明，所提出的算法复杂度较低，在满足用户异构需求的同时，有效降低了系统的长期能耗。