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Spectrum sensing for smart embedded devices in cognitive networks using machine learning algorithms

Saber Mohammed, El Rharras Abdessamad, Saadane Rachid, Chehri Abdellah, Hakem Nadir et Kharraz Hatim. (2020). Spectrum sensing for smart embedded devices in cognitive networks using machine learning algorithms. Procedia Computer Science, 176, p. 2404-2413.

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URL officielle: http://dx.doi.org/doi:10.1016/j.procs.2020.09.311

Résumé

Spectrum sensing is an essential step in cognitive radio-based dynamic spectrum management. Spectrum sensing to detect the presence of the licensed signals in a particular frequency band is one of the most important research topics in cognitive radio. To identify primary user (PU) presence, we propose a low cost and low power consumption implementation of spectrum sensing operation based on real signals. These signals are generated by smart embedded devices at 433 MHz wireless transmitter using ASK (Amplitude-Shift Keying) and FSK (Frequency-Shift Keying) modulation type. The reception interface is constructed using an RTL-SDR dongle connected to MATLAB software. The signal detection is done by using four techniques: the artificial neural network (ANN), support vector machine (SVM), Decision Trees (TREE), and k-nearest neighbors (KNN). This article comparatively analyzed the performance of the classifiers to identify the best method for spectrum sensing between the three techniques. The performance evaluation of our proposed model is the probability of detection (Pd) and the false alarm probability (Pfa). Results show also that the sensing is susceptible to signal to noise ratio value. This comparative study has been demonstrated that the spectrum sensing operation by ANN and SVM can be more accurate than KNN, TREE, and some other classical detectors.

Type de document:Article publié dans une revue avec comité d'évaluation
Volume:176
Pages:p. 2404-2413
Version évaluée par les pairs:Oui
Date:2020
Sujets:Sciences naturelles et génie > Génie
Sciences naturelles et génie > Génie > Génie informatique et génie logiciel
Sciences naturelles et génie > Sciences appliquées
Département, module, service et unité de recherche:Départements et modules > Département des sciences appliquées > Module d'ingénierie
Mots-clés:cognitive radio, spectrum sensing, artificial neural network, support vector machine, Decision Trees-nearest neighbors, RTL-SDR, ASK, FSK, radio cognitive, détection de spectre, réseau neuronal artificiel, machine à vecteurs de soutien, arbres de décision-voisins les plus proches
Déposé le:17 mai 2021 17:28
Dernière modification:17 mai 2021 17:28
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Creative Commons LicenseSauf indication contraire, les documents archivés dans Constellation sont rendus disponibles selon les termes de la licence Creative Commons "Paternité, pas d'utilisation commerciale, pas de modification" 2.5 Canada.

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