Transform-Based Multiresolution Decomposition for Unsupervised Learning and Data Clustering of Cellular Network Behavior
The growing complexity of cellular networks makes it harder for network operators to control 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 Analysi...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10763485/ |
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| author | Juan Cantizani-Estepa Sergio Fortes Javier Villegas Javier Rasines Raul Martin Cuerdo Raquel Barco |
| author_facet | Juan Cantizani-Estepa Sergio Fortes Javier Villegas Javier Rasines Raul Martin Cuerdo Raquel Barco |
| author_sort | Juan Cantizani-Estepa |
| collection | DOAJ |
| description | The growing complexity of cellular networks makes it harder for network operators to control 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 with pre-initialized Gaussian Mixture Models (GMMs) for the totally unsupervised grouping of cellular network behaviors using different metrics. 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, being useful for cluster analysis by experts as different duration issues are now segregated and automatically labeled. The generated labels are indicative of the intensity and duration of the anomalies, such labeling can be linguistic or visual, providing faster issue identification. The proposed approach has been tested with real network data, successfully separating different behaviors analyzed in the evaluation section of the manuscript. |
| format | Article |
| id | doaj-art-45fd20a4af074debad5c84ea82dc84d0 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-45fd20a4af074debad5c84ea82dc84d02025-08-20T02:48:54ZengIEEEIEEE Access2169-35362024-01-011217950617951510.1109/ACCESS.2024.350483010763485Transform-Based Multiresolution Decomposition for Unsupervised Learning and Data Clustering of Cellular Network BehaviorJuan Cantizani-Estepa0https://orcid.org/0000-0002-9337-8912Sergio Fortes1https://orcid.org/0000-0002-5857-6403Javier Villegas2https://orcid.org/0000-0002-5799-8540Javier Rasines3Raul Martin Cuerdo4Raquel Barco5https://orcid.org/0000-0002-8993-5229Telecommunication Research Institute (TELMA), E.T.S. Ingeniería de Telecomunicación, University of Málaga, Málaga, SpainTelecommunication Research Institute (TELMA), E.T.S. Ingeniería de Telecomunicación, University of Málaga, Málaga, SpainTelecommunication Research Institute (TELMA), E.T.S. Ingeniería de Telecomunicación, University of Málaga, Málaga, SpainEricsson-GAIA Sweden, Kista, SwedenEricsson-NDO SW Research and Development, Madrid, SpainTelecommunication Research Institute (TELMA), E.T.S. Ingeniería de Telecomunicación, University of Málaga, Málaga, SpainThe growing complexity of cellular networks makes it harder for network operators to control 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 with pre-initialized Gaussian Mixture Models (GMMs) for the totally unsupervised grouping of cellular network behaviors using different metrics. 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, being useful for cluster analysis by experts as different duration issues are now segregated and automatically labeled. The generated labels are indicative of the intensity and duration of the anomalies, such labeling can be linguistic or visual, providing faster issue identification. The proposed approach has been tested with real network data, successfully separating different behaviors analyzed in the evaluation section of the manuscript.https://ieeexplore.ieee.org/document/10763485/Anomaly detectioncellular networksclusteringmultiresolution analysis (MRA)Gaussian mixture model (GMM) |
| spellingShingle | Juan Cantizani-Estepa Sergio Fortes Javier Villegas Javier Rasines Raul Martin Cuerdo Raquel Barco Transform-Based Multiresolution Decomposition for Unsupervised Learning and Data Clustering of Cellular Network Behavior IEEE Access Anomaly detection cellular networks clustering multiresolution analysis (MRA) Gaussian mixture model (GMM) |
| title | Transform-Based Multiresolution Decomposition for Unsupervised Learning and Data Clustering of Cellular Network Behavior |
| title_full | Transform-Based Multiresolution Decomposition for Unsupervised Learning and Data Clustering of Cellular Network Behavior |
| title_fullStr | Transform-Based Multiresolution Decomposition for Unsupervised Learning and Data Clustering of Cellular Network Behavior |
| title_full_unstemmed | Transform-Based Multiresolution Decomposition for Unsupervised Learning and Data Clustering of Cellular Network Behavior |
| title_short | Transform-Based Multiresolution Decomposition for Unsupervised Learning and Data Clustering of Cellular Network Behavior |
| title_sort | transform based multiresolution decomposition for unsupervised learning and data clustering of cellular network behavior |
| topic | Anomaly detection cellular networks clustering multiresolution analysis (MRA) Gaussian mixture model (GMM) |
| url | https://ieeexplore.ieee.org/document/10763485/ |
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