| Title | QoE-Driven Anomaly Detection in Self-Organizing Mobile Networks Using Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Murudkar, Chetana V., Gitlin, Richard D. |
| Conference Name | 2019 Wireless Telecommunications Symposium (WTS) |
| Date Published | apr |
| Keywords | anomaly detection, composability, Computational modeling, Data models, Decision trees, dysfunctional serving eNodeBs, dysfunctional serving nodes, end-user experience, green mobile communication networks, learning (artificial intelligence), machine learning, mobile computing, mobile radio, network scenario, network-centric approaches, ns-3, ns-3 network simulator, parametric QoE model, Predictive models, pubcrawl, qoe, QoE-driven anomaly detection, quality of experience, resilience, Resiliency, self-healing networks, self-organizing mobile networks, SON, system model, telecommunication computing, user-centric approach |
| 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. |
| DOI | 10.1109/WTS.2019.8715528 |
| Citation Key | murudkar_qoe-driven_2019 |
QoE-Driven Anomaly Detection in Self-Organizing Mobile Networks Using Machine Learning