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- Anomaly Prevision in Radio Access Networks Using Functional Data Analysis hal link

Auteur(s): Ben Slimen Y., Allio Sylvain, Jacques J.

Conference: IEEE GlobeCom 2017 (Singapour, SG, 2017-12-04)
Actes de conférence: , vol. p. (2017)


Ref HAL: hal-01613475_v1
Exporter : BibTex | endNote
Résumé:

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.