A survey of machine learning algorithms and their applications in cognitive radio

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

doi:10.1007/978-3-319-24540-9_66

A survey of machine learning algorithms and their applications in cognitive radio

Mustafa Alshawaqfeh
, Xu Wang
, Ali Rıza Ekti
, Muhammad Zeeshan Shakir
, Khalid Qaraqe
, Erchin Serpedin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

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.

Original language	English
Title of host publication	Cognitive Radio Oriented Wireless Networks - 10th International Conference, CROWNCOM 2015, Revised Selected Papers
Editors	Mark Weichold, Muhammad Zeeshan Shakir, Mohamed Abdallah, Muhammad Ismail, Mounir Hamdi, George K. Karagiannidis
Publisher	Springer Verlag
Pages	790-801
Number of pages	12
ISBN (Print)	9783319245393
DOIs	https://doi.org/10.1007/978-3-319-24540-9_66
State	Published - 2015
Externally published	Yes
Event	10th International Conference on Cognitive Radio Oriented Wireless Networks, CROWNCOM 2015 - Doha, Qatar Duration: Apr 21 2015 → Apr 23 2015

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	156
ISSN (Print)	1867-8211

Conference

Conference	10th International Conference on Cognitive Radio Oriented Wireless Networks, CROWNCOM 2015
Country/Territory	Qatar
City	Doha
Period	04/21/15 → 04/23/15

Funding

This publication was made possible by NPRP grant 4-1293-2- 513 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Keywords

Cognitive radio
Learning engine
Machine learning
Modulation classification
Spectrum sensing

Access to Document

10.1007/978-3-319-24540-9_66

Cite this

Alshawaqfeh, M., Wang, X., Ekti, A. R., Shakir, M. Z., Qaraqe, K., & Serpedin, E. (2015). A survey of machine learning algorithms and their applications in cognitive radio. In M. Weichold, M. Z. Shakir, M. Abdallah, M. Ismail, M. Hamdi, & G. K. Karagiannidis (Eds.), Cognitive Radio Oriented Wireless Networks - 10th International Conference, CROWNCOM 2015, Revised Selected Papers (pp. 790-801). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 156). Springer Verlag. https://doi.org/10.1007/978-3-319-24540-9_66

Alshawaqfeh, Mustafa ; Wang, Xu ; Ekti, Ali Rıza et al. / A survey of machine learning algorithms and their applications in cognitive radio. Cognitive Radio Oriented Wireless Networks - 10th International Conference, CROWNCOM 2015, Revised Selected Papers. editor / Mark Weichold ; Muhammad Zeeshan Shakir ; Mohamed Abdallah ; Muhammad Ismail ; Mounir Hamdi ; George K. Karagiannidis. Springer Verlag, 2015. pp. 790-801 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{e44fb1d0c59a45f3a37435dc4f3a2f52,

title = "A survey of machine learning algorithms and their applications in cognitive radio",

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, Learning engine, Machine learning, Modulation classification, Spectrum sensing",

author = "Mustafa Alshawaqfeh and Xu Wang and Ekti, \{Ali Rıza\} and Shakir, \{Muhammad Zeeshan\} and Khalid Qaraqe and Erchin Serpedin",

note = "Publisher Copyright: {\textcopyright} Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015.; 10th International Conference on Cognitive Radio Oriented Wireless Networks, CROWNCOM 2015 ; Conference date: 21-04-2015 Through 23-04-2015",

year = "2015",

doi = "10.1007/978-3-319-24540-9\_66",

language = "English",

isbn = "9783319245393",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

publisher = "Springer Verlag",

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editor = "Mark Weichold and Shakir, \{Muhammad Zeeshan\} and Mohamed Abdallah and Muhammad Ismail and Mounir Hamdi and Karagiannidis, \{George K.\}",

booktitle = "Cognitive Radio Oriented Wireless Networks - 10th International Conference, CROWNCOM 2015, Revised Selected Papers",

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Alshawaqfeh, M, Wang, X, Ekti, AR, Shakir, MZ, Qaraqe, K & Serpedin, E 2015, A survey of machine learning algorithms and their applications in cognitive radio. in M Weichold, MZ Shakir, M Abdallah, M Ismail, M Hamdi & GK Karagiannidis (eds), Cognitive Radio Oriented Wireless Networks - 10th International Conference, CROWNCOM 2015, Revised Selected Papers. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 156, Springer Verlag, pp. 790-801, 10th International Conference on Cognitive Radio Oriented Wireless Networks, CROWNCOM 2015, Doha, Qatar, 04/21/15. https://doi.org/10.1007/978-3-319-24540-9_66

A survey of machine learning algorithms and their applications in cognitive radio. / Alshawaqfeh, Mustafa; Wang, Xu; Ekti, Ali Rıza et al.
Cognitive Radio Oriented Wireless Networks - 10th International Conference, CROWNCOM 2015, Revised Selected Papers. ed. / Mark Weichold; Muhammad Zeeshan Shakir; Mohamed Abdallah; Muhammad Ismail; Mounir Hamdi; George K. Karagiannidis. Springer Verlag, 2015. p. 790-801 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 156).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wang, Xu

AU - Ekti, Ali Rıza

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AU - Qaraqe, Khalid

AU - Serpedin, Erchin

N1 - Publisher Copyright: © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015.

PY - 2015

Y1 - 2015

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AB - 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.

KW - Cognitive radio

KW - Learning engine

KW - Machine learning

KW - Modulation classification

KW - Spectrum sensing

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ER -

Alshawaqfeh M, Wang X, Ekti AR, Shakir MZ, Qaraqe K, Serpedin E. A survey of machine learning algorithms and their applications in cognitive radio. In Weichold M, Shakir MZ, Abdallah M, Ismail M, Hamdi M, Karagiannidis GK, editors, Cognitive Radio Oriented Wireless Networks - 10th International Conference, CROWNCOM 2015, Revised Selected Papers. Springer Verlag. 2015. p. 790-801. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-319-24540-9_66

A survey of machine learning algorithms and their applications in cognitive radio

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