| dc.description.abstract | In regards to wireless communications, due to the explosion in demand for higher capacity
networks, availability of free spectrum resources have become increasingly scarce. The UHF
spectrum band in particular, due to its excellent electromagnetic properties, has been reported
as inefficiently used and congested by many spectrum regulators of the world. This spectrum
resource scarcity issue combined with the ongoing research and development for more intelligent,
autonomous and self-aware radio communication led to a vast amount of research on the concept
of Cognitive Radio.
Spectrum sensing plays a significant role in enabling 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 Logistic Regression, K-Nearest
Neighbor and Naives Bayes which are studied to find the best technique with the highest
classification accuracy (CA).
Ten (10) different locations around Makerere University our case study and 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 the RF explorer software that is installed on the
computer. With the complete data set, we cleaned the data set, the data set was preprocessed
and then divided it into \Test" and \Train" data.
Numerical results show that techniques Logistic Regression and K-Nearest Neighbor are the
best algorithm among all the supervised classifiers. We later on designed a web application
which can be accessed by the user through a web browser with an active internet connection
to easily understand all the aspects about our project. | en_US |
