Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks

2013 ◽  
Vol 31 (11) ◽  
pp. 2209-2221 ◽  
Author(s):  
Karaputugala Madushan Thilina ◽  
Kae Won Choi ◽  
Nazmus Saquib ◽  
Ekram Hossain
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey on Machine Learning-based Methods

2020 ◽  
Vol 3 ◽  
pp. 36-46
Author(s):  
Sundous Khamayseh ◽  
Alaa Halawani
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Continuous Growth ◽  
Radio Technology ◽  
Promising Solution

The continuous growth of demand experienced by wireless networks creates a spectrum availability challenge. Cognitive radio (CR) is a promising solution capable of overcoming spectrum scarcity. It is an intelligent radio technology that may be programmed and dynamically configured to avoid interference and congestion in cognitive radio networks (CRN). Spectrum sensing (SS) is a cognitive radio life cycle task aiming to detect spectrum holes. A number of innovative approaches are devised to monitor the spectrum and to determine when these holes are present. The purpose of this survey is to investigate some of these schemes which are constructed based on machine learning concepts and principles. In addition, this review aims to present a general classification of these machine learningbased schemes

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