Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey on Machine Learning-based Methods

Authors

DOI:

https://doi.org/10.26636/jtit.2020.137219

Keywords:

cognitive radio, cooperative spectrum sensing, IEEE 802.22, machine learning, spectrum sensing

Abstract

The continuous growth of demand experienced by wireless networks creates a spectrum availability challenge. Cognitive radio (CR) is a promising solution capable of overcoming spectrum scarcity. It is an intelligent radio technology that may be programmed and dynamically configured to avoid interference and congestion in cognitive radio networks (CRN). Spectrum sensing (SS) is a cognitive radio life cycle task aiming to detect spectrum holes. A number of innovative approaches are devised to monitor the spectrum and to determine when these holes are present. The purpose of this survey is to investigate some of these schemes which are constructed based on machine learning concepts and principles. In addition, this review aims to present a general classification of these machine learningbased schemes.

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Submitted

2023-05-22

Published

2020-09-30

Issue

No. 3 (2020)

Section

ARTICLES FROM THIS ISSUE

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Copyright (c) 2020 Journal of Telecommunications and Information Technology

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
S. Khamayseh and A. Halawani, “Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey on Machine Learning-based Methods”, JTIT, vol. 81, no. 3, pp. 36–46, Sep. 2020, doi: 10.26636/jtit.2020.137219.