Anomaly Prevision in Radio Access Networks Using Functional Data Analysis

Abstract : In order to help the network maintainers with the daily diagnosis and optimization tasks, a supervised model for mobile anomalies prevention is proposed. The objective is to detect future malfunctions of a set of cells, by only observing key performance indicators that are considered as functional data. Thus, by alerting the engineers as well as self-organizing networks, mobile operators can be saved from a certain performance degradation. The model has proven its efficiency with an application on real data that aims to detect capacity degradation, accessibility and call drops anomalies for LTE networks.
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Communication dans un congrès
IEEE GlobeCom 2017, Dec 2017, Singapour, Singapore. 2017
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Yosra Ben Slimen, Sylvain Allio, Julien Jacques. Anomaly Prevision in Radio Access Networks Using Functional Data Analysis. IEEE GlobeCom 2017, Dec 2017, Singapour, Singapore. 2017. 〈hal-01613475〉

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