Network planning tool based on network classification and load prediction

Seif eddine Hammami; Hossam Afifi; Michel Marot; Vincent Gauthier

Network planning tool based on network classification and load prediction

Networking and Internet Architecture 2016-02-02 v1

Authors: Seif eddine Hammami , Hossam Afifi , Michel Marot , Vincent Gauthier

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

support vector machine network protocol machine learning

Cite

@article{arxiv.1602.00448,
  title  = {Network planning tool based on network classification and load prediction},
  author = {Seif eddine Hammami and Hossam Afifi and Michel Marot and Vincent Gauthier},
  journal= {arXiv preprint arXiv:1602.00448},
  year   = {2016}
}

Comments

The article has 6 pages, 8 figures and is accepted to be presented at WCNC'16

Network planning tool based on network classification and load prediction

Abstract

Keywords

Cite

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