A Machine Learning Approach to Improve Spectrum Utilization in Cognitive Radio Networks.

Abstract

Spectrum sensing plays a significant role in enabling the utilization of spectrum holes by unlicensed secondary users in cognitive radio networks. Most of the related work concerning spectrum sensing has focused on sensing carried out by incoming secondary users aiming at locating spectrum opportunities. In this project, we analyze how to improve spectrum utilization in cognitive radio networks (CRNs) using different supervised machine learning techniques decision tree (DT), support vector machine (SVM), linear regression (LR) which are studied to find the best technique with the highest classification accuracy (CA). Using ten (10) different locations around Makerere University as our case study, we manually collected one thousand (1000) data sets from each location making a complete data set of 10,000 samples using an RF explorer coupled with RF explorer software that we installed on the computer. With a complete data set, we cleaned the data set and the data set was processed and then divided into \Test" and \Train" data. Numerical results show that SVM is the best algorithm among all the supervised classifiers. In order to have a visual and user-friendly results study and analysis, we designed an online web application.