Abstract
SummaryIn this paper, we introduce the implementation of the stationary wavelet transform in compressive sensing, particularly in spectrum sensing and edge detection in cognitive radio. We also review the different forms of the compressive sensing basis matrix, providing a comparative study of their performance, with emphasis on the matrices implementing the discrete wavelet transform, the stationary wavelet transform, and their multiscale counterparts. The results presented in this paper show that, using the stationary wavelet transform, we can reach the performance level and error rate obtained by the discrete wavelet transform using only half the samples required by the latter to attain the same performance. Copyright © 2016 John Wiley & Sons, Ltd.
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