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Journal of Emerging Trends in Computing and Information Sciences >> Call for Papers Vol. 8 No. 3, March 2017

Journal of Emerging Trends in Computing and Information Sciences

Compressed Spectrum Sensing in Cognitive Radio Network Based on Measurement Matrix

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Author Gh.Reza Armand, Ali Shahzadi, Hadi Soltanizadeh
ISSN 2079-8407
On Pages 126-133
Volume No. 5
Issue No. 2
Issue Date February 28, 2014
Publishing Date February 28, 2014
Keywords Cognitive radio, compressive sampling, spectrum sensing, measurement matrix


Compressive sampling (CS), or compressive sensing, has the ability for reconstructing a sparse signal with small number of measurements. There are some applications like spectrum sensing in cognitive radio which not necessarily need a perfect reconstruction. Consequently in this application, toward the decrement of high signal acquisition costs in wideband system, CS methods have been used for spectrum sensing. New developments in CS have presented a new way toward the reconstruction of the original signal by using minimum number of observations. In this paper, we present a novel method in which CS is employed for compressing spectrum sensing in CRNs. Also, we include the explanation for showing that how CS utilization actually can attain the advantage of sampling and computational complexity reduction at a same time. For simulating the compressive sensing application in Cognitive Radio network the measurement matrix made by some random numbers is multiplied in the spectrum which is occupied by users. The mentioned measurement matrix is chosen with a procedure in which by using an optimization technique the sparse spectrum can be precisely recovered. By using an available multiple optimization technique the spectrum can be reconstructed by small number of samples. MATLAB software is used for the simulation of the algorithm. A reliable Spectrum sensing, even in low SNR, and small number of samples, is confirmed by results of the simulation. These results demonstrate that this method can lead to a faster measuring range in comparison with other existing approaches.

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