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

Mustafa Alshawaqfeh; Xu Wang; Ali Ekti; Muhammad Shakir; Khalid Qaraqe; Erchin Serpedin

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

Download

439 downloads

Cite: BibTeX Plain Text

@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