Work

Network planning tool based on network classification and load prediction

Year: 2016

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1602.00448

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

Institution: Télécom SudParis

Language: English

Topic: Internet Traffic Analysis and Secure E-voting

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W2950443134

Cites: 24

Cited by: 1

Related to: 20