强化学习
计算机科学
移动边缘计算
计算卸载
资源配置
无线
边缘计算
分布式计算
无线网络
计算
服务器
最优化问题
GSM演进的增强数据速率
计算机网络
资源管理(计算)
人工智能
算法
电信
作者
Li Ji,Hui Gao,Tiejun Lv,Yueming Lu
出处
期刊:Wireless Communications and Networking Conference
日期:2018-04-01
被引量:421
标识
DOI:10.1109/wcnc.2018.8377343
摘要
Mobile edge computing (MEC) has the potential to enable computation-intensive applications in 5G networks. MEC can extend the computational capacity at the edge of wireless networks by migrating the computation-intensive tasks to the MEC server. In this paper, we consider a multi-user MEC system, where multiple user equipments (UEs) can perform computation offloading via wireless channels to an MEC server. We formulate the sum cost of delay and energy consumptions for all UEs as our optimization objective. In order to minimize the sum cost of the considered MEC system, we jointly optimize the offloading decision and computational resource allocation. However, it is challenging to obtain an optimal policy in such a dynamic system. Besides immediate reward, Reinforcement Learning (RL) also takes a long-term goal into consideration, which is very important to a time-variant dynamic systems, such as our considered multi-user wireless MEC system. To this end, we propose RL-based optimization framework to tackle the resource allocation in wireless MEC. Specifically, the Q-learning based and Deep Reinforcement Learning (DRL) based schemes are proposed, respectively. Simulation results show that the proposed scheme achieves significant reduction on the sum cost compared to other baselines.
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