Network planning tool based on network classification and load prediction

TL;DR

This paper presents a network planning tool that leverages CDR analysis, classification, and load prediction using SVM and SVR algorithms to optimize cellular network planning.

Contribution

It introduces an integrated approach combining classification and load prediction algorithms for improved network planning.

Findings

Classification accuracy of traffic classes using SVM and K-means

Effective load prediction with SVR for online traffic forecasting

Enhanced network planning capabilities for cellular operators

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.