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Communication dans un congrès

Blind Source Separation-based Full-Duplex Cognitive Radio

Abbass Nasser 1, 2, 3 Ali Mansour 1, 2 Koffi-Clément Yao 4, 5 Hassan Assaf 3 Hussein Abdallah 3
1 Lab-STICC_ENSTAB_CACS_COM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
2 Pôle STIC_IDM
ENSTA Bretagne - École Nationale Supérieure de Techniques Avancées Bretagne
5 Lab-STICC_UBO_CACS_COM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance, UBO - Université de Brest
Abstract : Full-Duplex has been emerged in Cognitive Radio Network in order to avoid the silence period of the Secondary User (SU) during the Spectrum Sensing. SU should monitor the Primary User (PU) activities in order to avoid any harmful interference. The conventional Full-Duplex Cognitive Radio (FDCR) systems are based on the Self-Interference Cancellation, where a problem of Residual Self-Interference and Hardware Imperfections leads to an important loss in the detection performance. In this paper, we develop spectrum sensing techniques for FD-CR based on the Blind Source Separation (BSS). In BSS, multi receiving antennas are required to detect the presence of the Primary User (PU) signal without the need for a silence period during the spectrum sensing. This fact enhances the data rate of the SU. In addition, this algorithms do not require any priori knowledge about the SU or the PU signal. Experimental results show that in addition to eliminating the silence period, the performance of our developed algorithms based on BSS outperforms the classical spectrum sensing Energy Detector (ED).
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https://hal.archives-ouvertes.fr/hal-01551460
Contributeur : Annick Billon-Coat <>
Soumis le : vendredi 30 juin 2017 - 12:04:01
Dernière modification le : mercredi 24 juin 2020 - 16:19:51

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  • HAL Id : hal-01551460, version 1

Citation

Abbass Nasser, Ali Mansour, Koffi-Clément Yao, Hassan Assaf, Hussein Abdallah. Blind Source Separation-based Full-Duplex Cognitive Radio. EEETEM2017, Apr 2017, Beyrouth, Lebanon. ⟨hal-01551460⟩

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