SQoE KQIs Anomaly Detection in Cellular Networks: Fast Online Detection Framework with Hourglass Clustering

被引：0

作者：

Xiaowei Qin

Shuang Tang

Xiaohui Chen

DANDan Miao

Guo Wei

机构：

[1] KeyLaboratoryofWireless-OpticalCommunications,ChineseAcademyofSciences,SchoolofInformationScienceandTechnology,UniversityofScienceandTechnologyofChina

来源：

中国通信 | 2018年 / 15卷 / 10期

关键词：

big data; SQoE; anomaly detection; hourglass clustering; codebook;

D O I：

暂无

中图分类号：

TN929.5 [移动通信];

学科分类号：

080402 ; 080904 ; 0810 ; 081001 ;

摘要：

The explosive growth of data volume in mobile networks makes fast online diagnose a pressing search problem. In this paper, an object-oriented detection framework with a two-step clustering, named as Hourglass Clustering, is given. Where three object parameters are chosen as Synthetical Quality of Experience(SQo E) Key Quality Indicators(KQIs) to reflect accessibility, integrality, and maintainability of networks. Then, we choose represented Key Performance Indicators(r KPIs) as cause parameters with correlation analysis. For these two kinds of parameters, a hybrid algorithm combining the self-organizing map(SOM) and

引用

页码：25 / 37

页数：13

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