计算机科学
移动边缘计算
强化学习
轨迹优化
坐标下降
任务(项目管理)
趋同(经济学)
计算
资源配置
GSM演进的增强数据速率
过程(计算)
实时计算
块(置换群论)
人工智能
弹道
数学优化
最优控制
算法
天文
操作系统
物理
经济增长
数学
经济
计算机网络
管理
几何学
作者
Liang Wang,Kezhi Wang,Cunhua Pan,Wei Xu,Nauman Aslam,Arumugam Nallanathan
标识
DOI:10.1109/tmc.2021.3059691
摘要
In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they enable task offloading from user equipment (UE). We aim to minimize energy consumption of all UEs via optimizing user association, resource allocation and the trajectory of UAVs. To this end, we first propose a Convex optimizAtion based Trajectory control algorithm (CAT), which solves the problem in an iterative way by using block coordinate descent (BCD) method. Then, to make the real-time decision while taking into account the dynamics of the environment (i.e., UAV may take off from different locations), we propose a deep Reinforcement leArning based trajectory control algorithm (RAT). In RAT, we apply the Prioritized Experience Replay (PER) to improve the convergence of the training procedure. Different from the convex optimization based algorithm which may be susceptible to the initial points and requires iterations, RAT can be adapted to any taking off points of the UAVs and can obtain the solution more rapidly than CAT once training process has been completed. Simulation results show that the proposed CAT and RAT achieve the considerable performance and both outperform traditional algorithms.
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