Spectrum-Aware Mobile Edge Computing for UAVs Using Reinforcement Learning-猫眼课题宝

DOI：

10.1145/3453142.3491414

发表时间：

2021-12

期刊：

2021 IEEE/ACM Symposium on Edge Computing (SEC)

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Babak Badnava;Taejoon Kim;Kenny Cheung;Zaheer Ali;M. Hashemi

我们考虑使用移动边缘计算（MEC）的无人机（UAV）卸载任务的问题。在这种情况下，每个 UAV 都会决定将计算任务卸载到更强大的 MEC 服务器（例如基站），或在本地执行任务。在本文中，我们提出了一种频谱感知决策框架，以便每个代理可以动态选择可用通道之一进行卸载。为此，我们为无人机开发了一个深度强化学习（DRL）框架，以选择任务卸载的通道或在本地执行计算。在基于深度Q网络的数值结果中，我们将能量消耗和任务完成时间的组合视为奖励。基于低频段、中频段和高频段通道的仿真结果表明，DQN 智能体可以有效地学习环境并动态调整其行为，以最大化长期奖励。

We consider the problem of task offloading by unmanned aerial vehicles (UAV) using mobile edge computing (MEC). In this context, each UAV makes a decision to offload the computation task to a more powerful MEC server (e.g., base station), or to perform the task locally. In this paper, we propose a spectrum-aware decision-making framework such that each agent can dynamically select one of the available channels for offloading. To this end, we develop a deep reinforcement learning (DRL) framework for the UAVs to select the channel for task offloading or perform the computation locally. In the numerical results based on deep Q-network, we con-sider a combination of energy consumption and task completion time as the reward. Simulation results based on low-band, mid-band, and high-band channels demonstrate that the DQN agents efficiently learn the environment and dynamically adjust their actions to maximize the long-term reward.

Spectrum-Aware Mobile Edge Computing for UAVs Using Reinforcement Learning

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