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首页> 外文期刊>Mobile networks & applications >Root Cause Analysis for Self-organizing Cellular Network: an Active Learning Approach
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Root Cause Analysis for Self-organizing Cellular Network: an Active Learning Approach

机译：自组织蜂窝网络的根本原因分析：一种积极的学习方法

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摘要

To ease the configuration and maintenance of complex cellular networks, the self-organizing network (SON) is introduced. SON contains three major sub-functional groups: self-configuration, self-optimization, and self-healing. Among these, fault diagnosis in self-healing is crucial, and it is usually considered as a classification problem which is commonly addressed by supervised machine learning methods. However, the barrier to these methods is the difficulty in obtaining sufficient network fault data with label (fault cause). To achieve an effective classifier and dramatically reduce the number of labeled instances needed, we propose an active learning based fault diagnosis scheme, which can select unlabeled instances for labeling actively. According to the selection criteria, there are several query strategies. In this paper, we apply uncertainty sampling as the query strategy due to its low computational cost and high efficiency. Besides, we implement random sampling as a contrast which is a nonactive learning method. To verify the effectiveness of the proposed scheme, we construct a long term evolution (LTE) system level simulator by Network Simulator 3. Then several fault scenarios are simulated, and the records of key performance indicators with fault causes are collected. Extensive experiments demonstrate that the proposed scheme is effective in reducing the number of labeled instances needed, and it is also valid in the class-imbalanced data. Specifically, to achieve a classifier with an accuracy of 99%, the active learning based method only needs 74 labeled instances but the nonactive learning method needs 1354 ones.
机译：为了简化复杂蜂窝网络的配置和维护，介绍了自组织网络（儿子）。儿子包含三个主要副官能团体：自我配置，自我优化和自我修复。其中，自我愈合的故障诊断至关重要，通常被认为是由监督机器学习方法常见的分类问题。然而，这些方法的障碍是难以使用标签（故障原因）获得足够的网络故障数据。为了实现有效的分类器并显着减少所需的标记实例的数量，我们提出了一种基于主动学习的故障诊断方案，可以积极地选择未标记的标签实例。根据选择标准，有几种查询策略。在本文中，由于其计算成本低，效率高，我们将不确定性取样作为查询策略。此外，我们将随机抽样实施为一个是非活动学习方法的对比度。为了验证所提出的方案的有效性，我们通过网络模拟器3构建长期演进（LTE）系统级模拟器3.然后模拟了几种故障场景，并收集具有故障原因的关键性能指示符的记录。广泛的实验表明，所提出的方案有效减少所需的标记实例的数量，并且它在类别 - 不平衡数据中也有效。具体地，为了实现精度为99％的分类器，基于活动学习的方法仅需要74标记的实例，但非活动学习方法需要1354个。

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  • 来源
    《Mobile networks & applications》 |2020年第6期|2506-2516|共11页
  • 作者

    Chen Meng; Zhu Kun; Chen Bing;

  • 作者单位

    Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Collaborat Innovat Ctr Novel Software Technol & I Nanjing Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Collaborat Innovat Ctr Novel Software Technol & I Nanjing Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Collaborat Innovat Ctr Novel Software Technol & I Nanjing Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Active learning; Self-healing; Fault diagnosis; XGBoost;

    机译：积极学习;自我修复;故障诊断;XGBoost;

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