A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario

被引:5
作者
Mason, Federico [1 ]
Drago, Matteo [1 ]
Zugno, Tommaso [1 ,2 ]
Giordani, Marco [1 ]
Boban, Mate [2 ]
Zorzi, Michele [1 ]
机构
[1] Univ Padua, Dept Informat Engn, Padua, Italy
[2] Munich Res Ctr, Huawei Technol, Munich, Germany
来源
2022 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) | 2022年
关键词
Predictive Quality of Service (PQoS); teleoperated driving; reinforcement learning (RL); RAN; ns-3;
D O I
10.1109/WCNC51071.2022.9771590
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
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, integrated at the RAN level that implements PQoS functionalities with the support of an RL framework. 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 better results in terms of QoS and Quality of Experience (QoE) performance of end users in a teleoperated driving scenario.
引用
收藏
页码:114 / 119
页数:6
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