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arXiv · 1602.00448

Network planning tool based on network classification and load prediction

Seif eddine Hammami·Hossam Afifi·Michel Marot·Vincent Gauthier
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Abstract

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.

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Seif eddine Hammami, Hossam Afifi, Michel Marot, Vincent Gauthier. 2016-02-01. Network planning tool based on network classification and load prediction. https://arxiv.org/abs/1602.00448

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