机器学习在网络路测质差小区分析中的应用
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山东省重大科技创新工程(2019JZZY010120);山东省重点研发计划(2019GSF111054)


Application of Machine Learning in Poor Cell Analysis of Network Drive Test
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    摘要:

    由于LTE网络数据量庞大而且种类繁多,人工路测分析已经无法满足当今对基于路测数据质差小区检测的需求.为了提高质差小区检测的效率与正确率,机器学习逐渐在质差小区检测中得到了应用.本文针对小区数量较少的路测数据,提出了一种基于距离的四维特征的质差小区检测方法.该方法采用聚类算法和人工判断相结合的方式对路测数据进行标定,对比分析了基于距离的四维特征和传统的两维特征的提取效果,并在逻辑回归分类器、决策树分类器、支持向量机分类器和k近邻分类器这4种分类器中进行分类.实验结果表明,基于距离的四维特征比传统的二维特征更有利于质差小区检测;使用四维特征进行分类,支持向量机分类器的效果最好.

    Abstract:

    Due to the large amount and variety of LTE network data, the manual drive test analysis has been unable to meet the current requirements for poor quality cell detection based on drive test data. In order to improve the efficiency and accuracy of the poor quality cell detection, machine learning is gradually applied in the detection of poor quality cell. In this study, a poor quality cell detection method based on four-dimensional feature of distance is proposed for the small number of road survey data. This method uses clustering algorithm and artificial judgment to calibrate road test data. And it compares the extraction effect of the distance based four-dimensional features and the traditional two-dimensional features. The featuresare classified by logistic regression classifier, decision tree classifier, support vector machine classifier and k-nearest neighbor classifier. The experimental results show that the distance-based four-dimensional features are more beneficial to the detection of quality difference cells than the traditional two-dimensional features. Support vector machine classifier works best when four-dimensional features are used for classification.

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邵星,许鸿奎,李鑫,姜彤彤.机器学习在网络路测质差小区分析中的应用.计算机系统应用,2020,29(5):257-263

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  • 收稿日期:2019-10-10
  • 最后修改日期:2019-11-04
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  • 在线发布日期: 2020-05-07
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