loading page

Transform-based Multiresolution Decomposition for Unsupervised Learning and Data Clustering of Cellular Network Behaviour
  • +4
  • Juan Cantizani-Estepa ,
  • Sergio Fortes ,
  • Javier Villegas ,
  • Javier Rasines Suarez ,
  • Raúl Martín Cuerdo ,
  • Eduardo Baena ,
  • Raquel Barco
Juan Cantizani-Estepa
Universidad de Málaga

Corresponding Author:[email protected]

Author Profile
Sergio Fortes
Author Profile
Javier Villegas
Author Profile
Javier Rasines Suarez
Author Profile
Raúl Martín Cuerdo
Author Profile
Eduardo Baena
Author Profile
Raquel Barco
Author Profile

Abstract

The growing complexity of cellular networks makes it harder for network operators to monitor and manage the system. To ease the management and automatically detect network problems, unsupervised techniques have been put to use. This work proposes a novel method that combines Multi-Resolution Analysis (MRA) by wavelet transforms and unsupervised clustering for the totally unsupervised grouping of cellular network behaviours through different Key-Performance Indicator (KPI)s. The application of multi-resolution decomposition, allows the much simpler clustering technique to take into account temporal information that would require of a much complex method otherwise. The proposed approach has been tested with real network data successfully separating different behaviours.