5G/B5G Service Classification Using Supervised Learning

被引:8
|
作者
Preciado-Velasco, Jorge E. [1 ]
Gonzalez-Franco, Joan D. [2 ]
Anias-Calderon, Caridad E. [2 ]
Nieto-Hipolito, Juan I. [3 ]
Rivera-Rodriguez, Raul [4 ]
机构
[1] CICESE Res Ctr, Dept Elect & Telecommun, Carretera Ensenada Tijuana 3918, Ensenada 22860, BC, Mexico
[2] La Havana Technol Univ CUJAE, Fac Elect & Telecommun, Calle 114, Marianao 19390, La Havana, Cuba
[3] FIAD Autonomous Univ Baja California, Fac Engn Architecture & Design, Carretera Ensenada Tijuana 3917, Ensenada 22860, BC, Mexico
[4] CICESE Res Ctr, Div Telemat, Carretera Ensenada Tijuana 3918, Ensenada 22860, BC, Mexico
来源
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 11期
关键词
5G; B5G service classification; ML; predictive model; KPI; KQI; QoS; QoE; SLA;
D O I
10.3390/app11114942
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The classification of services in 5G/B5G (Beyond 5G) networks has become important for telecommunications service providers, who face the challenge of simultaneously offering a better Quality of Service (QoS) in their networks and a better Quality of Experience (QoE) to users. Service classification allows 5G service providers to accurately select the network slices for each service, thereby improving the QoS of the network and the QoE perceived by users, and ensuring compliance with the Service Level Agreement (SLA). Some projects have developed systems for classifying these services based on the Key Performance Indicators (KPIs) that characterize the different services. However, Key Quality Indicators (KQIs) are also significant in 5G networks, although these are generally not considered. We propose a service classifier that uses a Machine Learning (ML) approach based on Supervised Learning (SL) to improve classification and to support a better distribution of resources and traffic over 5G/B5G based networks. We carry out simulations of our proposed scheme using different SL algorithms, first with KPIs alone and then incorporating KQIs and show that the latter achieves better prediction, with an accuracy of 97% and a Matthews correlation coefficient of 96.6% with a Random Forest classifier.
引用
收藏
页数:19
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