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QoE-driven Anomaly Detection in Self-Organizing Mobile Networks using Machine Learning | IEEE Conference Publication | IEEE Xplore

QoE-driven Anomaly Detection in Self-Organizing Mobile Networks using Machine Learning


Abstract:

Current procedures for anomaly detection in self-organizing mobile communication networks use network-centric approaches to identify dysfunctional serving nodes. In this ...Show More

Abstract:

Current procedures for anomaly detection in self-organizing mobile communication networks use network-centric approaches to identify dysfunctional serving nodes. In this paper, a user-centric approach and a novel methodology for anomaly detection is proposed, where the Quality of Experience (QoE) metric is used to evaluate the end-user experience. The system model demonstrates how dysfunctional serving eNodeBs are successfully detected by implementing a parametric QoE model using machine learning for prediction of user QoE in a network scenario created by the ns-3 network simulator. This approach can play a vital role in the future ultra-dense and green mobile communication networks that are expected to be both self- organizing and self-healing.
Date of Conference: 09-12 April 2019
Date Added to IEEE Xplore: 27 October 2019
ISBN Information:
Print on Demand(PoD) ISSN: 1934-5070
Conference Location: New York, NY, USA

References

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