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Compressive Spectrum Sensing: Wavelet-Based Compressive Spectrum Sensing in Cognitive Radio

Compressive Spectrum Sensing: Wavelet-Based Compressive Spectrum Sensing in Cognitive Radio

Said E. El-Khamy, Mina B. Abd el-Malek, Sara H. Kamel
Copyright: © 2019 |Pages: 26
ISBN13: 9781522553540|ISBN10: 1522553541|ISBN13 Softcover: 9781522587927|EISBN13: 9781522553557
DOI: 10.4018/978-1-5225-5354-0.ch012
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MLA

El-Khamy, Said E., et al. "Compressive Spectrum Sensing: Wavelet-Based Compressive Spectrum Sensing in Cognitive Radio." Sensing Techniques for Next Generation Cognitive Radio Networks, edited by Ashish Bagwari, et al., IGI Global, 2019, pp. 203-228. https://doi.org/10.4018/978-1-5225-5354-0.ch012

APA

El-Khamy, S. E., el-Malek, M. B., & Kamel, S. H. (2019). Compressive Spectrum Sensing: Wavelet-Based Compressive Spectrum Sensing in Cognitive Radio. In A. Bagwari, J. Bagwari, & G. Tomar (Eds.), Sensing Techniques for Next Generation Cognitive Radio Networks (pp. 203-228). IGI Global. https://doi.org/10.4018/978-1-5225-5354-0.ch012

Chicago

El-Khamy, Said E., Mina B. Abd el-Malek, and Sara H. Kamel. "Compressive Spectrum Sensing: Wavelet-Based Compressive Spectrum Sensing in Cognitive Radio." In Sensing Techniques for Next Generation Cognitive Radio Networks, edited by Ashish Bagwari, Jyotshana Bagwari, and Geetam Singh Tomar, 203-228. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-5354-0.ch012

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Abstract

In this chapter, the authors discuss how compressive sensing can be used in wideband spectrum sensing in cognitive radio systems. Compressive sensing helps decrease the complexity and processing time and allows for higher data rates to be used, since it makes it possible for the signal to be sampled at rates lower than the Nyquist rate and still be reconstructed with high accuracy. Different sparsifying bases for compressive sensing are presented in this chapter and their performance is compared. The design of these matrices is based on different types of wavelet transforms, including the discrete wavelet transform and the stationary wavelet transform; the latter having shown a clear improvement in performance over the former. The authors present different ways of implementing these transforms in a compressive sensing framework. Additionally, different types of reconstruction methods including the genetic algorithm and the auxiliary function method are also presented and their impact on the overall performance is discussed.

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