A detection method for handover-related radio link failures based on SVM

Abstract

A new methodology based on Support Vector Machine (SVM) for the detection of handover-related radio link failures was presented. After analyzing the characteristics of three abnormal handovers, five handover-related events are extracted to describe abnormal HOs and five time points are set in HO procedure for ease of quantifying these events. Based on these data, the classification performance of the SVM-AID algorithm was tested and the effects of the parameters in SVM on the classification were analyzed. The experimental results show that the parameters should be chosen carefully because they have great effects on the classification. The simulation results also demonstrate that the proposed approach achieves the best efficiency and accuracy with Polynomial kernel function. This study provides a new idea and a basis of application for anomaly detection in Self-Organizing Networks (SON). © 2014 Springer International Publishing.