Root Cause Analysis for Self-organizing Cellular Network: an Active Learning Approach

Abstract

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.