DocumentCode :
3567933
Title :
Blind spectrum detector for cognitive radio using compressed sensing and symmetry property of the second order cyclic autocorrelation
Author :
Khalaf, Ziad ; Nafkha, Amor ; Palicot, Jacques
Author_Institution :
SUPELEC/IETR, SUPELEC, Cesson-Sevigne, France
fYear :
2012
Firstpage :
291
Lastpage :
296
Abstract :
Based on the use of compressed sensing applied to recover the sparse cyclic autocorrelation (CA) in the cyclic frequencies domain on the one hand, and by exploiting the symmetry property of the cyclic autocorrelation on the other hand, this paper proposes a new totally blind narrow band spectrum sensing algorithm with relatively low complexity in order to detect free bands in the radio spectrum. This new sensing method uses only few iterations of the Orthogonal Matching Pursuit algorithm and have the particularity to perform robust detection with only few samples (short observation time). This new method outperforms the totally blind method proposed in [1] that only exploited the sparse property of the CA without requiring any additional calculation complexity for the same SNR and data samples number.
Keywords :
cognitive radio; compressed sensing; computational complexity; frequency-domain analysis; iterative methods; signal detection; blind spectrum detector; calculation complexity; cognitive radio; compressed sensing; cyclic frequency domain; free band detection; iterations; orthogonal matching pursuit algorithm; robust detection; second-order cyclic autocorrelation; sparse cyclic autocorrelation; symmetry property; totally-blind narrowband spectrum sensing algorithm; Complexity theory; Compressed sensing; Correlation; Detectors; Matching pursuit algorithms; Vectors; Cognitive Radio; Compressed Sensing; Detection Features; Orthogonal Matching Pursuit; Sparsity; Spectrum Sensing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), 2012 7th International ICST Conference on
ISSN :
2166-5370
Print_ISBN :
978-1-4673-2976-7
Electronic_ISBN :
2166-5370
Type :
conf
Filename :
6333756
Link To Document :
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