@inproceedings{a2f3ffc706ce4ab9a4ce7343504f29fd,
title = "Network planning tool based on network classification and load prediction",
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.",
keywords = "CDR, Classification, K-means, Network planning tool, Prediction, SVM, SVR, traffic Load",
author = "Hammami, \{Seif Eddine\} and Hossam Afifi and Michel Marot and Vincent Gauthier",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Wireless Communications and Networking Conference, WCNC 2016 ; Conference date: 03-04-2016 Through 07-04-2016",
year = "2016",
month = jan,
day = "1",
doi = "10.1109/WCNC.2016.7565166",
language = "English",
series = "IEEE Wireless Communications and Networking Conference, WCNC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 IEEE Wireless Communications and Networking Conference, WCNC 2016",
}