论文标题
带有多运营商支持的基于雾计算的车辆系统的深入加固学习
Deep Reinforcement Learning for Fog Computing-based Vehicular System with Multi-operator Support
论文作者
论文摘要
本文研究了可以通过为云/雾计算连接的车辆系统启用多操作员无线连接来实现的潜在性能改善。移动网络运营商（MNO）选择和切换问题是通过考虑开关成本，服务质量（QoS）变化以及MNO之间的不同价格来提出的，这些价格由不同的MNO以及云和雾服务器收取的不同价格。提出了基于双重Q网络（DQN）的开关策略，并证明能够以保证的延迟和可靠性绩效最小化每辆车的长期平均成本。使用在市售城市范围内LTE网络中收集的数据集评估所提出的方法的性能。仿真结果表明，我们提出的政策可以大大降低使用保证的延迟服务的每种雾/云连接车辆所支付的成本。
This paper studies the potential performance improvement that can be achieved by enabling multi-operator wireless connectivity for cloud/fog computing-connected vehicular systems. Mobile network operator (MNO) selection and switching problem is formulated by jointly considering switching cost, quality-of-service (QoS) variations between MNOs, and the different prices that can be charged by different MNOs as well as cloud and fog servers. A double deep Q network (DQN) based switching policy is proposed and proved to be able to minimize the long-term average cost of each vehicle with guaranteed latency and reliability performance. The performance of the proposed approach is evaluated using the dataset collected in a commercially available city-wide LTE network. Simulation results show that our proposed policy can significantly reduce the cost paid by each fog/cloud-connected vehicle with guaranteed latency services.
