Synthetical QoE-Driven Anomalous Cell Pattern Detection with a Hybrid Algorithm

机译：用杂交算法综合QoE驱动的异常细胞模式检测

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Owing to more attention on quality of service (QoS) for subscribers, the mobile operators should shift evaluation standard from QoS to Quality of Experience (QoE). However, most researches in the field focus on end-to-end metrics, few of them consider synthetical QoE in the whole network. For mobile carriers, it is more significative to improve the overall system performance at the lowest cost. Therefore, the comprehensive evaluation of all users is more suitable for network optimization. As voice is still the basic service, we consider anomaly detection about voice service in this paper. Firstly, two synthetical QoE parameters, quality of voice (QoV) and successful rate of wireless access ( WA), are considered to identify abnormalities of cells from the aspect of integrality and accessibility respectively. Then, we use a hybrid algorithm combining self-organizing map ( SOM) and K-means to classify abnormal data points into several categories. After that, the data points for cells are treated as time series to compute the proportions in each anomaly model, which form anomalous cell patterns. To location where the exception happened accurately, the other 5 Key Performance Indicators (KPIs) are selected by association Rule according to the correlation between two synthetical QoE parameters. They are used to identify specific classes of faults. The experiment shows that the proposed method is effective to visualize and analyze anomalous cell patterns. It can be a guideline for the operators to perform faster and more efficient troubleshooting.

机译：由于更多关注用户的服务质量（QoS），移动运营商应将评估标准从QoS转换为经验质量（QoE）。然而，大多数研究领域的研究专注于端到端指标，其中很少有人考虑整个网络中的综合QoE。对于移动载体，更重要的是以最低成本提高整体系统性能更为重要。因此，对所有用户的综合评估更适合网络优化。由于语音仍然是基本服务，我们考虑了本文的异常检测语音服务。首先，两种合成QoE参数，语音质量（QoV）和成功的无线接入率（WA）分别认为分别从整体和可访问性方面识别细胞的异常。然后，我们使用混合算法组合自组织地图（SOM）和K均值来将异常数据点分类为几个类别。之后，将细胞的数据点被视为时间序列以计算每个异常模型中的比例，其形成异常的细胞模式。对于例外的位置准确发生，根据两个合成QoE参数之间的相关性，通过关联规则选择其他5个关键性能指标（KPI）。它们用于识别特定的故障类别。该实验表明，该方法是有效可视化和分析异常细胞模式。它可以是运算符执行更快更有效的故障排除的指导。

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《International Conference on Information Technology - New Generations》|2016年|xxi 1306 pages :|共11页
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Dandan Miao; Weijian Sun; Xiaowei Qin; Weidong Wang;
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中图分类 TP14-532;
关键词
Data mining; SOM; KPIs; Synthetical QoE; Anomalous cell pattern;

机译：数据挖掘;SOM;kpis;综合qoe;异常细胞模式;

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Synthetical QoE-Driven Anomalous Cell Pattern Detection with a Hybrid Algorithm

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