遥控驾驶场景下PQoS的强化学习框架
A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario
摘要：
近年来，已经考虑到预测服务质量（PQoS）来设计自治网络，作为在工业和/或汽车领域中操作的应用预测未预期的服务质量（QoS）变化并相应地作出反应的手段。在这种背景下，强化学习（RL）已经成为一种很有前途的方法来执行准确的预测，并优化无线网络的效率和适应性。沿着这些路线，在本文中，我们提出了一个新的实体的设计，实现在RAN级的RL框架的支持下，实现PQoS功能。具体来说，我们专注于学习代理的奖励函数的设计，能够转换成适当的对策，如果QoS要求不满足QoS估计。我们证明，通过ns-3模拟，我们的方法实现了最佳的权衡QoS和体验质量（QoE）性能的终端用户在远程操作驾驶类的情况下，相比其他基线解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
