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.

为了简化复杂蜂窝网络的配置和维护，引入了自组织网络（SON）。SON包含三个主要的子功能组：自我配置，自我优化和自我修复。其中，自我修复中的故障诊断至关重要，通常被认为是有监督的机器学习方法通​​常解决的分类问题。但是，这些方法的障碍是难以获得带有标签（故障原因）的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数量，我们提出了一种基于主动学习的故障诊断方案，该方案可以选择未标记实例进行主动标记。根据选择标准，有几种查询策略。在本文中，由于不确定性采样的计算成本低，效率高，因此我们将其作为查询策略。此外，我们采用随机抽样作为对比，这是一种非主动学习方法。为了验证所提方案的有效性，我们通过网络模拟器3构建了一个长期演进（LTE）系统级模拟器。然后，模拟了几种故障情况，并收集了具有故障原因的关键性能指标的记录。大量实验表明，该方案可以有效减少所需的带标签实例数量，并且在类不平衡数据中也有效。具体而言，为了实现精度为99％的分类器，基于主动学习的方法仅需要74个标记实例，而非主动学习方法则需要1354个实例。