Network planning tool based on network classification and load prediction

Seif Eddine Hammami (1, 2, 3) , Hossam Afifi (4, 1, 2, 3) , Michel Marot (5, 1, 2, 3) , Vincent Gauthier (4, 1, 2, 3)

1 TSP - RST - Département Réseaux et Services de Télécommunications
2 SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
3 CNRS - Centre National de la Recherche Scientifique
4 R3S-SAMOVAR - Réseaux, Systèmes, Services, Sécurité
5 METHODES-SAMOVAR - Méthodes et modèles pour les réseaux

Real Call Detail Records (CDR) are analyzed and classified based on Support Vector Machine (SVM) algorithm. The daily classification results in three traffic classes. We use two different algorithms, K-means and SVM to check the classification efficiency. A second support vector regression (SVR) based algorithm is built to make an online prediction of traffic load using the history of CDRs. Then, these algorithms will be integrated to a network planning tool which will help cellular operators on planning optimally their access network

Mots clés: CDR; Classification; SVM; Network planning tool; Traffic Load; K-means

Domaines: Réseaux et télécommunications [cs.NI]

Seif Eddine Hammami, Hossam Afifi, Michel Marot, Vincent Gauthier. Network planning tool based on network classification and load prediction. WCNC 2016 : IEEE Wireless Communications and Networking Conference, Apr 2016, Doha, Qatar. pp.1 - 6, ⟨10.1109/WCNC.2016.7565166⟩. ⟨hal-01370148⟩