A stationary wavelet transform approach to compressed spectrum sensing in cognitive radio. (2nd May 2016) - El‐Khamy, Said E.; Abd‐el‐Malek, Mina B.; Kamel, Sara H.
Summary: In 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. Abstract : The stationary wavelet transform provides a better sparsifying domain for compressive sensing. Using it as a basis matrix gives improved performance results and more accurate estimates of the channel boundaries in cognitive radio. 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.