SVM Assisted Primary User-Detection for Non-Cooperative Cognitive Radio Networks

Bouallegue, Kais; Crussière, Matthieu; Kharbech, Sofiane

Document type :

Communication dans un congrès avec actes

DOI :

10.1109/ISCC50000.2020.9219601

Title :

SVM Assisted Primary User-Detection for Non-Cooperative Cognitive Radio Networks

Author(s) :

Bouallegue, Kais [Auteur]
Institut d'Électronique et des Technologies du numéRique [IETR]
Crussière, Matthieu [Auteur]
Institut d'Électronique et des Technologies du numéRique [IETR]
Kharbech, Sofiane [Auteur]
Institut de Recherche sur les Composants logiciels et matériels pour l'Information et la Communication Avancée - UAR 3380 [IRCICA]
Université de Tunis El Manar [UTM]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]

Conference title :

2020 IEEE Symposium on Computers and Communications, ISCC 2020

City :

Rennes

Country :

France

Start date of the conference :

2020-07-07

Journal title :

Proceedings - IEEE Symposium on Computers and Communications

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Publication date :

2020

English keyword(s) :

Cognitive radio
eigenvalue decomposition
spectrum sensing
supportvector machines

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

This paper presents a new blind spectrum sensing (SS) algorithm based on a machine learning model: the radial basis function support-vector machines (RBF-SVM). As features, the introduced approach uses statistical tests ...
Show more >This paper presents a new blind spectrum sensing (SS) algorithm based on a machine learning model: the radial basis function support-vector machines (RBF-SVM). As features, the introduced approach uses statistical tests that are based on the eigenvalues of the received signals covariance matrix. Since the decision on the frequency resource occupancy is in fact an issue of labeling binary data, SVM is intended as a potential technique for SS paradigm. The flexibility of SVM for linearly non-separable and high dimensional data makes it a good candidate for our issue, particularly that we consider low signal to noise ratios (SNR). Computer simulations shows that the proposal outperforms classical non-cooperative SS algorithms. © 2020 IEEE.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL

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