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A Cognitive Radio Spectrum Sensing Implementation Based on Deep Learning and Real Signals

Saber Mohamed, Chehri Abdellah, El Rharras Abdessamad, Saadane Rachid et Wahbi Mohammed. (2021). A Cognitive Radio Spectrum Sensing Implementation Based on Deep Learning and Real Signals. Dans Mohamed Ben Ahmed, Ismail Rakıp Karaș, Domingos Santos, Olga Sergeyeva et Anouar Abdelhakim Boudhir (dir.), The Proceedings of the 5th International Conference on Smart City Applications, volume 4. (p. 930-941). Lecture Notes in Networks and Systems. Cham, Switzerland : Spriger.

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Résumé

In a cognitive radio environment, spectrum sensing is an essential phase for improving spectrum resources management. Based on a deep learning method and real signals, a new spectrum sensing implementation is proposed in this work. The real signals are artificially generated, using an ARDUINO UNO card and a 433 MHz wireless transmitter, in ASK and FSK modulation types. The reception interface is constructed using an RTL-SDR receiver connected to MATLAB software. The signals classification is carried out by a convolutional neural network (CNN) classifier. Our proposed model’s main objective is to identify the spectrum state (free or occupied) by classifying the received signals into a licensed user (primary user) signals or noise signals. Our proposed model’s performance evaluation is evaluated by two metrics: the probability of detection (Pd) and the false alarm probability (PFA). Finally, the proposed sensing method is compared with other used techniques for signal classification, such as energy detection, artificial neural network, and support vector machine. The experimental results show that CNN could classify the real signals better than traditional methods and machine learning methods.

Type de document:Chapitre de livre
Date:2021
Lieu de publication:Cham, Switzerland
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
Éditeurs:Ben Ahmed, Mohamed
Rakıp Karaș, Ismail
Santos, Domingos
Sergeyeva, Olga
Boudhir, Anouar Abdelhakim
Liens connexes:
Mots-clés:cognitive radio network, spectrum sensing, CNN, RTL-SDR, ASK-FSK signals, réseau radio cognitif, détection de spectre, signaux, proceedings
Déposé le:03 mai 2022 22:16
Dernière modification:03 mai 2022 22:16
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