Predictive Quality of Service: The Next Frontier for Fully Autonomous Systems

被引:16
作者
Boban, Mate [1 ]
Giordani, Marco [2 ]
Zorzi, Michele [3 ]
机构
[1] Huawei Technol Duesseldorf GmbH, Dusseldorf, Germany
[2] Univ Padua, Padua, Italy
[3] Univ Padua, Informat Engn Dept, Wireless Commun Res, Padua, Italy
来源
IEEE NETWORK | 2021年 / 35卷 / 06期
关键词
Autonomous systems; Quality of service; Machine learning; Predictive models; Hardware; Machinery;
D O I
10.1109/MNET.001.2100237
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Recent advances in software, hardware, computing, and control have fueled significant prog-ress in the field of autonomous systems. Notably, autonomous machines should continuously estimate how the scenario in which they move and operate will evolve within a predefined timeframe, and foresee whether or not the network will be able to fulfill the agreed quality of service (QoS). If not, appropriate countermea-sures should be taken to satisfy the application requirements. Along these lines, in this article we present possible methods to enable predictive QoS (PQoS) in autonomous systems, and discuss which use cases will particularly benefit from network prediction. Then we shed light on the challenges in the field that are still open for future research. As a case study, we demonstrate whether machine learning can facilitate PQoS in a teleoperated-driving-like use case as a function of different measurement signals.
引用
收藏
页码:104 / 110
页数:7
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