Cooperative Bayesian Compressed Spectrum Sensing for Correlated Wideband Signals

Honggyu JUNG  Kwang-Yul KIM  Yoan SHIN  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E97-A   No.6   pp.1434-1438
Publication Date: 2014/06/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E97.A.1434
Type of Manuscript: LETTER
Category: Communication Theory and Signals
cognitive radio,  spectrum sensing,  compressed sensing,  sparse Bayesian learning,  multiple measurement vector,  

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We propose a cooperative compressed spectrum sensing scheme for correlated signals in wideband cognitive radio networks. In order to design a reconstruction algorithm which accurately recover the wideband signals from the compressed samples in low SNR (Signal-to-Noise Ratio) environments, we consider the multiple measurement vector model exploiting a sequence of input signals and propose a cooperative sparse Bayesian learning algorithm which models the temporal correlation of the input signals. Simulation results show that the proposed scheme outperforms existing compressed sensing algorithms for low SNRs.