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Cognitive Radio Oriented Wireless Networks. 10th International Conference, CROWNCOM 2015, Doha, Qatar, April 21–23, 2015, Revised Selected Papers

Research Article

A Survey of Machine Learning Algorithms and Their Applications in Cognitive Radio

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  • @INPROCEEDINGS{10.1007/978-3-319-24540-9_66,
        author={Mustafa Alshawaqfeh and Xu Wang and Ali Ekti and Muhammad Shakir and Khalid Qaraqe and Erchin Serpedin},
        title={A Survey of Machine Learning Algorithms and Their Applications in Cognitive Radio},
        proceedings={Cognitive Radio Oriented Wireless Networks. 10th International Conference, CROWNCOM 2015, Doha, Qatar, April 21--23, 2015, Revised Selected Papers},
        proceedings_a={CROWNCOM},
        year={2015},
        month={10},
        keywords={Cognitive radio Machine learning Learning engine Spectrum sensing Modulation classification},
        doi={10.1007/978-3-319-24540-9_66}
    }
    
  • Mustafa Alshawaqfeh
    Xu Wang
    Ali Ekti
    Muhammad Shakir
    Khalid Qaraqe
    Erchin Serpedin
    Year: 2015
    A Survey of Machine Learning Algorithms and Their Applications in Cognitive Radio
    CROWNCOM
    Springer
    DOI: 10.1007/978-3-319-24540-9_66
Mustafa Alshawaqfeh1,*, Xu Wang1,*, Ali Ekti1,*, Muhammad Shakir2,*, Khalid Qaraqe2,*, Erchin Serpedin1,*
  • 1: Texas A&M University
  • 2: Texas A&M University at Qatar
*Contact email: mustafa.shawaqfeh@tamu.edu, xu.wang@tamu.edu, arekti@tamu.edu, muhammad.shakir@qatar.tamu.edu, khalid.qaraqe@qatar.tamu.edu, serpedin@ece.tamu.edu

Abstract

Cognitive radio (CR) technology is a promising candidate for next generation intelligent wireless networks. The cognitive engine plays the role of the brain for the CR and the learning engine is its core. In order to fully exploit the features of CRs, the learning engine should be improved. Therefore, in this study, we discuss several machine learning algorithms and their applications for CRs in terms of spectrum sensing, modulation classification and power allocation.

Keywords
Cognitive radio Machine learning Learning engine Spectrum sensing Modulation classification
Published
2015-10-13
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-319-24540-9_66
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