Multiband spectrum sensing for cognitive radios based on distributed compressed measurements

A wideband spectrum sensing method for cognitive radios is presented which is based on compressed measurements. The proposed detector does not require signal reconstruction from the compressed measurements. A fusion centre collects the measurements from different sensing nodes and then makes a sensing decision based on a simplified maximum likelihood criterion which does not require prior signal information. This results in an efficient and low complexity spectrum detector especially for dynamic spectrum occupancy scenarios. The performance of the proposed detector is exhibited by means of numerical simulations for probability of erroneous detection and receiver operating characteristic curves.

[1]  André Bourdoux,et al.  Multiband maximum likelihood signal detection based on compressive measurements , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

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

[3]  Geert Leus,et al.  A Study on Cooperative Compressive Wideband Power Spectrum Sensing , 2012 .

[4]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[5]  Yonina C. Eldar,et al.  Sub-Nyquist processing with the modulated wideband converter , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

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

[7]  R.G. Baraniuk,et al.  Distributed Compressed Sensing of Jointly Sparse Signals , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[8]  Cynthia S. Hood,et al.  Spectral Occupancy and Interference Studies in support of Cognitive Radio Technology Deployment , 2006, 2006 1st IEEE Workshop on Networking Technologies for Software Defined Radio Networks.

[9]  Geert Leus,et al.  Compressive Sampling-Based Multiple Symbol Differential Detection for UWB Communications , 2014, IEEE Transactions on Wireless Communications.

[10]  S. M. Mishra,et al.  Spectrum Sensing : Fundamental Limits , 2009 .

[11]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[12]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[13]  Geert Leus,et al.  Distributed compressive wide-band spectrum sensing , 2009, 2009 Information Theory and Applications Workshop.

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

[15]  Geert Leus,et al.  Compressive Sampling Based Energy Detection of Ultra-Wideband Pulse Position Modulation , 2013, IEEE Transactions on Signal Processing.

[16]  Richard G. Baraniuk,et al.  Signal Processing With Compressive Measurements , 2010, IEEE Journal of Selected Topics in Signal Processing.