Anomaly Prevision in Radio Access Networks Using Functional Data Analysis

Yosra Ben Slimen (1, 2) , Sylvain Allio (1) , Julien Jacques (2)

1 Orange Labs - Orange Labs [Belfort]
2 ERIC - Entrepôts, Représentation et Ingénierie des Connaissances

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.

Mots clés: Anomaly prevention; Multivariate functional; Data prevision; Functional data analysis; LTE network; Troubleshooting; Optimization

Domaines: Statistiques [math.ST]

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⟩