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.

[1]  J. Fuchs.  More on sparse representations in arbitrary bases , 2003 .

[2]  S. Mallat,et al.  Adaptive greedy approximations , 1997 .

[3]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[4]  Georgios B. Giannakis,et al.  Statistical tests for presence of cyclostationarity , 1994, IEEE Trans. Signal Process..

[5]  Jean Jacques Fuchs.  Identification of real sinusoids in noise, the global matched filter approach. , 2009 .

[6]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[7]  A. Sonnenschein,et al.  Radiometric detection of spreadspectrum signals in noise of uncertain power , 1992 .

[8]  Yonghong Zeng,et al.  A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions , 2010, EURASIP J. Adv. Signal Process..

[9]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[10]  William A. Gardner,et al.  Signal interception: performance advantages of cyclic-feature detectors , 1992, IEEE Trans. Commun..

[11]  Wassim Jouini,et al.  Energy Detection Limits Under Log-Normal Approximated Noise Uncertainty , 2011, IEEE Signal Processing Letters.

[12]  S. Sitharama Iyengar,et al.  A Novel Robust Detection Algorithm for Spectrum Sensing , 2011, IEEE Journal on Selected Areas in Communications.

[13]  Stéphane Mallat,et al.  A Wavelet Tour of Signal Processing - The Sparse Way, 3rd Edition , 2008 .

[14]  Jacques Palicot,et al.  Blind spectrum detector for cognitive radio using compressed sensing and symmetry property of the second order cyclic autocorrelation , 2012, 2012 7th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM).

[15]  J. Fuchs.  Sparse representations and realization theory , 2006 .

[16]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[17]  Philippe Ciblat,et al.  Cognitive OFDM system detection using pilot tones second and third-order cyclostationarity , 2011, Signal Process..

[18]  Geoffrey Ye Li,et al.  Ten years of research in spectrum sensing and sharing in cognitive radio , 2012, EURASIP J. Wirel. Commun. Netw..

[19]  William A. Gardner,et al.  Statistical spectral analysis : a nonprobabilistic theory , 1986 .

[20]  Houman Owhadi,et al.  A non-adapted sparse approximation of PDEs with stochastic inputs , 2010, J. Comput. Phys..

[21]  Rémi Gribonval,et al.  Sparse representations in unions of bases , 2003, IEEE Trans. Inf. Theory.

[22]  Jacques Palicot,et al.  Blind cyclostationary feature detector based on sparsity hypotheses for cognitive radio equipment , 2011, 2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS).

[23]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.