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Bayesian Compressive Sensing with Circulant Matrix for Spectrum Sensing in Cognitive Radio Networks. (arXiv:1802.03457v1 [eess.SP])
来源于:arXiv
For wideband spectrum sensing, compressive sensing has been proposed as a
solution to speed up the high dimensional signals sensing and reduce the
computational complexity. Compressive sensing consists of acquiring the
essential information from a sparse signal and recovering it at the receiver
based on an efficient sampling matrix and a reconstruction technique. In order
to deal with the uncertainty, improve the signal acquisition performance, and
reduce the randomness during the sensing and reconstruction processes,
compressive sensing requires a robust sampling matrix and an efficient
reconstruction technique. In this paper, we propose an approach that combines
the advantages of a Circulant matrix with Bayesian models. This approach is
implemented, extensively tested, and its results have been compared to those of
l1 norm minimization with a Circulant or random matrix based on several
metrics. These metrics are Mean Square Error, reconstruction error,
correlation, recovery time, sam 查看全文>>