ICTACT Journal on Communication Technology

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Comparative Analysis of Energy Detection and Artificial Neural Network for Spectrum Sensing in Cognitive Radio


Affiliations
1 Department of Electronics and Telecommunication Engineering, Pune Institute of Computer Technology, India
     

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In today’s wireless communication technology, spectrum occupancy is one of the major challenge. To perform all the task in wireless communication intelligently, Cognitive Radio (CR) is used. With the help of machine learning techniques, performance of CR will increase. In this paper, implementation of spectrum sensing (SS) in Cognitive Radio Network (CRN) is presented. To check the availability of spectrum, the supervised Machine Learning (ML) and conventional spectrum sensing method is used. To classify signal and noise, the Artificial Neural Network (ANN) classifier is used. The classifier’s result shows better result than conventional method’s result.

Keywords

Machine Learning, Cognitive Radio Network, Cognitive Radio Network.
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  • Comparative Analysis of Energy Detection and Artificial Neural Network for Spectrum Sensing in Cognitive Radio

Abstract Views: 297  |  PDF Views: 0

Authors

Sanjog Shah
Department of Electronics and Telecommunication Engineering, Pune Institute of Computer Technology, India
R. G. Yelalwar
Department of Electronics and Telecommunication Engineering, Pune Institute of Computer Technology, India

Abstract


In today’s wireless communication technology, spectrum occupancy is one of the major challenge. To perform all the task in wireless communication intelligently, Cognitive Radio (CR) is used. With the help of machine learning techniques, performance of CR will increase. In this paper, implementation of spectrum sensing (SS) in Cognitive Radio Network (CRN) is presented. To check the availability of spectrum, the supervised Machine Learning (ML) and conventional spectrum sensing method is used. To classify signal and noise, the Artificial Neural Network (ANN) classifier is used. The classifier’s result shows better result than conventional method’s result.

Keywords


Machine Learning, Cognitive Radio Network, Cognitive Radio Network.

References