A Detection Method for Handover-Related Radio Link Failures Based on SVM
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).
KeywordsMRO SVM handover SON detection
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- 1.3GPP TS 36.300, v11.3.0, Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Overall description (2011)Google Scholar
- 2.Zhang, Y., Zhou, W., Wald-Hauser, R.: Method of determining a radio link failure associated with a handover of a user equipment from a source access node to a target access node, access node for determining a radio link failure associated with a handover of a user equipment from a source access node to a target access node, and user equipment. China Patent Agent (H.K.)Ltd. (2011)Google Scholar
- 5.Kecman, V.: Learning and Soft Computing — Support Vector Machines, Neural Networks, Fuzzy Logic Systems. The MIT Press, Cambridge (2001)Google Scholar
- 6.Vapnik, V.: The Nature of Statistical Learning Theory. Springer (1995)Google Scholar
- 7.Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines Software (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
- 8.4G Americas. Self-Optimizing Networks - The Benefits of SON in LTE (July 2011)Google Scholar
- 9.de Morsier, F., Tuia, D., Borgeaud, M., Gass, V., Thiran, J.-P.: Semi-Supervised Novelty Detection Using SVM Entire Solution Path. IEEE Transactions on Geoscience and Remote Sensing PP(99), 1–12 (2013)Google Scholar