New algorithms for wideband spectrum sensing via compressive sensing

We consider the problem of spectrum sensing in a Cognitive Radio (CR) system when the primaries can be occupying a few subbands in a wideband spectrum. Since the primary signal dimension is large, Nyquist rate can be very high. Compressive sensing (CS) can be useful in this setup. However a CR system needs to operate at a very low SNR(~ -20dB) where the compressive sensing techniques are usually not successful. Combining them with statistical techniques can be useful. But this has been difficult because the statistics of the parameters obtained from the recovery algorithms (e.g., OMP) are not available. We develop a suboptimal recovery algorithm COR for which the statistics can be easily approximated. This allows us to use Neyman Pearson technique as well as sequential detection techniques with CS. The resulting algorithms provide satisfactory performance at -20 dB SNR. In fact COR's recovery performance is better than OMP itself at low SNR. We also modify the algorithm for the scenario when the channel gains and the noise variance may also not be available.

[1]  Lie Wang,et al.  Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise , 2011, IEEE Transactions on Information Theory.

[2]  T. Lai.  Nearly Optimal Sequential Tests of Composite Hypotheses , 1988 .

[3]  Richard G. Baraniuk,et al.  Detection and estimation with compressive measurements , 2006 .

[4]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[5]  Hüseyin Arslan,et al.  A survey of spectrum sensing algorithms for cognitive radio applications , 2009, IEEE Communications Surveys & Tutorials.

[6]  C. Cordeiro,et al.  Spectrum agile radios: utilization and sensing architectures , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[7]  Zhi Tian,et al.  Cyclic Feature Based Wideband Spectrum Sensing Using Compressive Sampling , 2011, 2011 IEEE International Conference on Communications (ICC).

[8]  Georgios B. Giannakis,et al.  Compressed Sensing for Wideband Cognitive Radios , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[9]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[10]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[11]  Olgica Milenkovic,et al.  Subspace Pursuit for Compressive Sensing: Closing the Gap Between Performance and Complexity , 2008, ArXiv.

[12]  H. Nikookar,et al.  A survey on spectrum sensing techniques for cognitive radio , 2009, 2009 Second International Workshop on Cognitive Radio and Advanced Spectrum Management.

[13]  Hongyi Yu,et al.  The Detection of Frequency Hopping Signal Using Compressive Sensing , 2009, 2009 International Conference on Information Engineering and Computer Science.

[14]  H. Urkowitz.  Energy detection of unknown deterministic signals , 1967 .

[15]  Deanna Needell,et al.  Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit , 2007, IEEE Journal of Selected Topics in Signal Processing.

[16]  Dmitry M. Malioutov,et al.  Sequential Compressed Sensing , 2010, IEEE Journal of Selected Topics in Signal Processing.

[17]  Erik G. Larsson,et al.  Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances , 2012, IEEE Signal Processing Magazine.

[18]  Zhi Tian,et al.  Compressed Wideband Sensing in Cooperative Cognitive Radio Networks , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

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

[20]  Shuguang Cui,et al.  Collaborative wideband sensing for cognitive radios , 2008, IEEE Signal Processing Magazine.

[21]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[22]  David L. Donoho,et al.  Sparse Solution Of Underdetermined Linear Equations By Stagewise Orthogonal Matching Pursuit , 2006 .

[23]  Danijela Cabric,et al.  Cyclostationary-based low complexity wideband spectrum sensing using compressive sampling , 2012, 2012 IEEE International Conference on Communications (ICC).

[24]  J. Wolfowitz,et al.  Optimum Character of the Sequential Probability Ratio Test , 1948 .

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