论文标题
PQO的强化学习框架在遥控驾驶场景中
A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario
论文作者
论文摘要
近年来，自主网络已经考虑了预测的服务质量（PQO），作为在工业和/或汽车领域运行的应用程序的一种手段，以预测意外的服务质量（QOS）变化并做出相应的反应。在这种情况下，加强学习（RL）已成为执行准确预测并优化无线网络的效率和适应性的有前途的方法。沿着这些界限，在本文中，我们提出了一个新实体的设计，该设计在RAN级别上实施，在RL框架的支持下，实现了PQOS功能。具体而言，我们专注于学习代理的奖励功能的设计，如果不满足QoS要求，则能够将QoS估计值转换为适当的对策。我们通过NS-3模拟证明，与其他基线解决方案相比，我们的方法在QoS和经验质量（QOE）的最终用户的表现方面取决于最终用户的最佳权衡。
In recent years, autonomous networks have been designed with Predictive Quality of Service (PQoS) in mind, as a means for applications operating in the industrial and/or automotive sectors to predict unanticipated Quality of Service (QoS) changes and react accordingly. In this context, Reinforcement Learning (RL) has come out as a promising approach to perform accurate predictions, and optimize the efficiency and adaptability of wireless networks. Along these lines, in this paper we propose the design of a new entity, implemented at the RAN-level that, with the support of an RL framework, implements PQoS functionalities. Specifically, we focus on the design of the reward function of the learning agent, able to convert QoS estimates into appropriate countermeasures if QoS requirements are not satisfied. We demonstrate via ns-3 simulations that our approach achieves the best trade-off in terms of QoS and Quality of Experience (QoE) performance of end users in a teleoperated-driving-like scenario, compared to other baseline solutions.
