Comparing Machine Learning Algorithms for Improving the Maintenance of LTE Networks Based on Alarms Analysis  

作　　者：Batchakui Bernabe Deussom Djomadji Eric Michel Chana Anne Marie Mama Tsimi Serge Fabrice Batchakui Bernabe;Deussom Djomadji Eric Michel;Chana Anne Marie;Mama Tsimi Serge Fabrice(Department of Computer Engineering, National Advanced School of Engineering, UY1, Yaoundé, Cameroon;Department of Electrical and Telecommunications Engineering, National Advanced School of Engineering, Yaoundé, Cameroon;College of Technology, University of Buea, Buea, Cameroon)

机构地区：[1]Department of Computer Engineering, National Advanced School of Engineering, UY1, Yaoundé, Cameroon [2]Department of Electrical and Telecommunications Engineering, National Advanced School of Engineering, Yaoundé, Cameroon [3]College of Technology, University of Buea, Buea, Cameroon

出　　处：《Journal of Computer and Communications》2022年第12期125-137,共13页电脑和通信（英文）

摘　　要：Mobile network operators are facing many challenges to satisfy their subscribers in terms of quality of service and quality of experience provided. To achieve this goal, technological progress and scientific advances offer good opportunities for efficiency in the management of faults occurring in a mobile network. Machine learning techniques allow systems to learn from past experiences and can predict, solutions to be applied to correct the root cause of a failure. This paper evaluates machine learning techniques and identifies the decision tree as a learning model that provides the most optimal error rate in predicting outages that may occur in a mobile network. Three machine learning techniques are presented in this study and compared with regard to accuracy. This study demonstrates that the appropriate machine learning technique improves the accuracy of the model. By using the decision tree as a machine learning model, it was possible to predict solutions to network failures, with an error rate less than 2%. In addition, the use of Machine Learning makes it possible to eliminate steps in the network failure processing chain;resulting in reduced service disruption time and improved the network availability which is a key network performance index.Mobile network operators are facing many challenges to satisfy their subscribers in terms of quality of service and quality of experience provided. To achieve this goal, technological progress and scientific advances offer good opportunities for efficiency in the management of faults occurring in a mobile network. Machine learning techniques allow systems to learn from past experiences and can predict, solutions to be applied to correct the root cause of a failure. This paper evaluates machine learning techniques and identifies the decision tree as a learning model that provides the most optimal error rate in predicting outages that may occur in a mobile network. Three machine learning techniques are presented in this study and compared with regard to accuracy. This study demonstrates that the appropriate machine learning technique improves the accuracy of the model. By using the decision tree as a machine learning model, it was possible to predict solutions to network failures, with an error rate less than 2%. In addition, the use of Machine Learning makes it possible to eliminate steps in the network failure processing chain;resulting in reduced service disruption time and improved the network availability which is a key network performance index.

关 键 词：4G LTE Mobile Network Machine Learning Network Maintenance TROUBLESHOOTING Decision Tree Random Forest 

分 类 号：TN9[电子电信—信息与通信工程]