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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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Contributor : Julien Jacques Connect in order to contact the contributor
Submitted on : Monday, October 9, 2017 - 4:17:34 PM
Last modification on : Friday, April 15, 2022 - 3:00:04 PM
Long-term archiving on: : Wednesday, January 10, 2018 - 1:44:19 PM


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  • HAL Id : hal-01613475, version 1



Yosra Ben Slimen, Sylvain Allio, Julien Jacques. Anomaly Prevision in Radio Access Networks Using Functional Data Analysis. IEEE GlobeCom 2017, Dec 2017, Singapour, Singapore. ⟨hal-01613475⟩



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