Multi Feature Based Classifier for Spectrum Sensing in Cognitive Radio

Authors

  • V C.S Kaushik
  • S Kolangiammal
  • B E Manoj Kumar

DOI:

https://doi.org/10.14419/ijet.v7i3.12.16557

Published:

2018-07-20

Keywords:

Dynamic Spectrum Access, Cognitive Radio, Machine Learning, Spectrum Sensing, Multi Feature Based Classifier

Abstract

Cognitive Radio (CR) is an important technology which can enable the implementation of Dynamic Spectrum Access, which is a paradigm shift from the static spectrum access model. It is an intelligent wireless communication system which can sense the environment and can take decisions to effectively use the available radio resource without creating any interference to the Licensed Primary Users.   Hence sensing of the spectrum plays a very important role in the effective implementation of this technology. We propose a new spectrum sensing algorithm in this paper which is based on machine learning and uses a Multi Feature based Classifier (MFC) model for classification of the spectrum.

 

 

References

[1] Karaputugala Madushan Thilina, Kae Won Choi, Nazmus Saquib, and Ekram Hossain, “Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networksâ€, IEEE Journal on Selected Areas in Communications ( Volume: 31, Issue: 11, November 2013).

[2] Haozhou Xue and Feifei GAO, “A Machine Learning based Spectrum-Sensing Algorithm Using Sample Covariance Matrixâ€, 10th International Conference on Communications and Networking in China (China Com), 2015.

[3] Dong-Chul Park, “ Multiple Feature-based Classifier and Its Application to Image Classificationâ€, 2010 IEEE International Conference on Data Mining Workshops

[4] Ethem Alpaydin “Introduction to Machine Learning “, Third Edition

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How to Cite

C.S Kaushik, V., Kolangiammal, S., & E Manoj Kumar, B. (2018). Multi Feature Based Classifier for Spectrum Sensing in Cognitive Radio. International Journal of Engineering & Technology, 7(3.12), 894–898. https://doi.org/10.14419/ijet.v7i3.12.16557

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Received 2018-07-30
Accepted 2018-07-30
Published 2018-07-20