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 proceedingConference contribution

2 Citations (Scopus)

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 languageEnglish
Title of host publicationCognitive Radio Oriented Wireless Networks - 10th International Conference, CROWNCOM 2015, Revised Selected Papers
PublisherSpringer Verlag
Pages790-801
Number of pages12
Volume156
ISBN (Print)9783319245393
DOIs
Publication statusPublished - 2015
Event10th International Conference on Cognitive Radio Oriented Wireless Networks, CROWNCOM 2015 - Doha, Qatar
Duration: 21 Apr 201523 Apr 2015

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume156
ISSN (Print)18678211

Other

Other10th International Conference on Cognitive Radio Oriented Wireless Networks, CROWNCOM 2015
CountryQatar
CityDoha
Period21/4/1523/4/15

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Keywords

  • Cognitive radio
  • Learning engine
  • Machine learning
  • Modulation classification
  • Spectrum sensing

ASJC Scopus subject areas

  • Computer Networks and Communications

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 Cognitive Radio Oriented Wireless Networks - 10th International Conference, CROWNCOM 2015, Revised Selected Papers (Vol. 156, 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