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http://dx.doi.org/10.7840/kics.2013.38B.9.765

Cooperative Bayesian Compressed Spectrum Sensing for Correlated Signals in Cognitive Radio Networks  

Jung, Honggyu (숭실대학교 대학원 정보통신공학과)
Kim, Kwangyul (숭실대학교 대학원 정보통신공학과)
Shin, Yoan (숭실대학교 정보통신전자공학부)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.38B, no.9, 2013 , pp. 765-774 More about this Journal
Abstract
In this paper, we present a cooperative compressed spectrum sensing scheme for correlated signals in decentralized wideband cognitive radio networks. Compressed sensing is a signal processing technique that can recover signals which are sampled below the Nyquist rate with high probability, and can solve the necessity of high-speed analog-to-digital converter problem for wideband spectrum sensing. In compressed sensing, one of the main issues is to design recovery algorithms which accurately recover original signals from compressed signals. In this paper, in order to achieve high recovery performance, we consider the multiple measurement vector model which has a sequence of compressed signals, and propose a cooperative sparse Bayesian recovery algorithm which models the temporal correlation of the input signals.
Keywords
Cognitive Radio; Spectrum Sensing; Compressed Sensing; Sparse Bayesian Learning; Multiple Measurement Vector; Correlated Signals;
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