Transform-based Multiresolution Decomposition for Unsupervised Learning and Data Clustering of Cellular Network Behaviour.

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.