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Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey on Machine Learning-based Methods, Journal of Telecommunications and Information Technology, 2020, nr 3
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Title: Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey on Machine Learning-based Methods, Journal of Telecommunications and Information Technology, 2020, nr 3

Creator:

Khamayseh, Sundous ; Halawani, Alaa

Date:

2020, nr 3

Resource Type:

artykuł

Description:

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

Publisher:

Instytut Łączności - Państwowy Instytut Badawczy

Format:

application/pdf

Resource Identifier:

ISSN 1509-4553, on-line: ISSN 1899-8852 ; oai:bc.itl.waw.pl:2147

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności

Subject and keywords:

cognitive radio machine learning cooperative spectrum sensing spectrum sensing IEEE 802.22

Object collections:

Last modified:

Feb 25, 2026

In our library since:

Oct 22, 2020

Number of object content hits:

988

All available object's versions:

https://ribes-54.man.poznan.pl/publication/2443

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Edition name Date
Khamayseh, Sundous; Halawani, Alaa, Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey on Machine Learning-based Methods, Journal of Telecommunications and Information Technology, 2020, nr 3 Feb 25, 2026

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