Visible to the public QoE-Driven Anomaly Detection in Self-Organizing Mobile Networks Using Machine Learning

TitleQoE-Driven Anomaly Detection in Self-Organizing Mobile Networks Using Machine Learning
Publication TypeConference Paper
Year of Publication2019
AuthorsMurudkar, Chetana V., Gitlin, Richard D.
Conference Name2019 Wireless Telecommunications Symposium (WTS)
Date Publishedapr
Keywordsanomaly 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
AbstractCurrent 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.
Citation Keymurudkar_qoe-driven_2019