%0 Conference Proceedings
%T Anomaly Prevision in Radio Access Networks Using Functional Data Analysis
%+ Orange Labs [Belfort] (Orange Labs)
%+ Entrepôts, Représentation et Ingénierie des Connaissances (ERIC)
%A Ben Slimen, Yosra
%A Allio, Sylvain
%A Jacques, Julien
%< avec comité de lecture
%B IEEE GlobeCom 2017
%C Singapour, Singapore
%8 2017-12-04
%D 2017
%K Anomaly prevention
%K Multivariate functional
%K Data prevision
%K Functional data analysis
%K LTE network
%K Troubleshooting
%K Optimization
%Z Mathematics [math]/Statistics [math.ST]Conference papers
%X 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.
%G English
%2 https://inria.hal.science/hal-01613475/document
%2 https://inria.hal.science/hal-01613475/file/anomaly_prevision.pdf
%L hal-01613475
%U https://inria.hal.science/hal-01613475
%~ UNIV-LYON1
%~ UNIV-LYON2
%~ ERIC
%~ LABEXIMU
%~ LYON2
%~ UDL
%~ UNIV-LYON