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Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:GLOBECOM 2017 - 2017 IEEE GLOBAL COMMUNICATIONS CONFERENCE

Key Words:Self-healing Fault diagnosis Imbalanced data Synthetic minority oversampling technique (SMOTE) AdaBoost

Abstract:Self-organizing networks (SON) aim to offer high quality services while reducing both capital expenditure (CAPEX) and operational expenditure (OPEX). SON consists of three main functions: self-configuration, self-optimization, and self-healing. Comparing with self-configuration and self-optimization, there exits only few studies on self-healing. However, it plays an important role in maintaining network operation. Note that self-healing mainly includes fault detection, fault diagnosis, and fault compensation. In this paper, we focus on fault diagnosis and propose an ensemble learning based fault diagnosis system for a self-organizing cellular networks. Specifically, in the proposed ensemble learning framework, the base learner is strengthened in each iteration and the final diagnosis result is obtained from the combination of all base classifications. Moreover, traditional classification algorithms are designed considering the premise of balanced data set. However, when they are applied to imbalanced data, the classification accuracy for minority classes is not satisfying. To deal with imbalanced training data sets, we apply the synthetic minority over-sampling technique (SMOTE) in the proposed system, which could also alleviate the difficulties caused by insufficient fault data. Simulation results show that the proposed system can achieve a high diagnosis accuracy, which can be further improved with the increase of training samples. In addition, the diagnosis accuracy of minority fault classes can be significantly improved with the application of SMOTE.

ISSN No.:2334-0983

Translation or Not:no

Date of Publication:2017-01-01

Co-author:Sun, Mengyun,zhukun,Guan Donghai,F70206470

Correspondence Author:Qian Hongyan