Blind spectrum detector for cognitive radio using compressed sensing and symmetry property of the second order cyclic autocorrelation

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.