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Improving energy detection in cognitive radio systems using machine learning

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dc.contributor.author Fajemilehin, Temitope O.
dc.contributor.author Yahya, Abid
dc.contributor.author Langat, Kibet
dc.date.accessioned 2020-10-01T10:30:42Z
dc.date.available 2020-10-01T10:30:42Z
dc.date.issued 2020-01
dc.identifier.citation Fajemilehin, T. O., Yahya, A. and Langat, K. (2020) Improving energy detection in cognitive radio systems using machine learning. Journal of Communications, 15(1), 74- 80.10.12720/jcm.15.1.74-80 en_US
dc.identifier.issn 1796-2021
dc.identifier.issn 2374-4367
dc.identifier.uri http://repository.biust.ac.bw/handle/123456789/223
dc.description This is an open-access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made. en_US
dc.description.abstract Research has shown that a huge portion of the electromagnetic spectrum is underutilized. Over the years, cognitive radio has been demonstrated as an efficient dynamic spectrum management technique. Energy detection is one of the widely used spectrum sensing techniques. However, its performance is limited by factors such as multipath fading and shadowing, which makes it prone to errors, particularly in low signal-to-noise ratio conditions. Yet, it still has a low computational cost, which reduces communication overhead. This paper aims to improve the detection accuracy of the energy detector through the use of machine learning (ML) techniques. In this research, ML models were trained using the energy characteristics of the primary user and other users present within the system. Weighted KNN produced the highest overall accuracy with an average of 91.88% accuracy at various SNR conditions. However, complex tree algorithm gave the most accurate detection (99% accuracy) of the primary user across all the channel conditions tested. This detection also helped to differentiate between the identity of the primary or secondary user from interference. en_US
dc.language.iso en en_US
dc.publisher Engineering and Technology Publishing en_US
dc.subject Cognitive radio en_US
dc.subject Energy detection en_US
dc.subject Detection accuracy en_US
dc.subject Machine learning en_US
dc.title Improving energy detection in cognitive radio systems using machine learning en_US
dcterms.license CC BYNC-ND 4.0
dc.description.level phd en_US
dc.description.accessibility unrestricted en_US
dc.description.department cte en_US


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