Spectrum sensing is a key component in any cog- nitive radio network. Recently full-duplex communication, i.e., the ability to transmit and receive at the same time at the same frequency, has become feasible. Residual self interference is inevitable even after applying self interference cancellation techniques in radio frequency (RF) and baseband domains. In this paper, we study the performance of popular eigenvalue based spectrum sensing techniques under residual self interference. Moreover, we investigate their performance when exploiting the correlation coefficient matrix, rather than the covariance matrix, to estimate the decision statistics. Finally, we propose three new correlation coefficient matrix based algorithms that outperform existing techniques.
Performance of Eigenvalue Based Spectrum Sensing In Full-Duplex Cognitive Radio Networks / Badawy, AHMED MOHAMED HABELROMAN B M; Elfouly, Tarek; Khattab, Tamer; Chiasserini, Carla Fabiana; Trinchero, Daniele. - STAMPA. - (2016). (Intervento presentato al convegno THE 29TH ANNUAL IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE 2016) tenutosi a Vancouver (Canada) nel 15—18 MAY, 2016) [10.1109/CCECE.2016.7726605].
Performance of Eigenvalue Based Spectrum Sensing In Full-Duplex Cognitive Radio Networks
BADAWY, AHMED MOHAMED HABELROMAN B M;CHIASSERINI, Carla Fabiana;TRINCHERO, Daniele
2016
Abstract
Spectrum sensing is a key component in any cog- nitive radio network. Recently full-duplex communication, i.e., the ability to transmit and receive at the same time at the same frequency, has become feasible. Residual self interference is inevitable even after applying self interference cancellation techniques in radio frequency (RF) and baseband domains. In this paper, we study the performance of popular eigenvalue based spectrum sensing techniques under residual self interference. Moreover, we investigate their performance when exploiting the correlation coefficient matrix, rather than the covariance matrix, to estimate the decision statistics. Finally, we propose three new correlation coefficient matrix based algorithms that outperform existing techniques.Pubblicazioni consigliate
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https://hdl.handle.net/11583/2635812
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