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A Collaborative Approach for Compressive Spectrum Sensing

A Collaborative Approach for Compressive Spectrum Sensing

Ahmed M. Elzanati, Mohamed F. Abdelkader, Karim G. Seddik
Copyright: © 2015 |Pages: 26
ISBN13: 9781466665712|ISBN10: 1466665718|EISBN13: 9781466665729
DOI: 10.4018/978-1-4666-6571-2.ch006
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MLA

Elzanati, Ahmed M., et al. "A Collaborative Approach for Compressive Spectrum Sensing." Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management, edited by Naima Kaabouch and Wen-Chen Hu, IGI Global, 2015, pp. 153-178. https://doi.org/10.4018/978-1-4666-6571-2.ch006

APA

Elzanati, A. M., Abdelkader, M. F., & Seddik, K. G. (2015). A Collaborative Approach for Compressive Spectrum Sensing. In N. Kaabouch & W. Hu (Eds.), Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management (pp. 153-178). IGI Global. https://doi.org/10.4018/978-1-4666-6571-2.ch006

Chicago

Elzanati, Ahmed M., Mohamed F. Abdelkader, and Karim G. Seddik. "A Collaborative Approach for Compressive Spectrum Sensing." In Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management, edited by Naima Kaabouch and Wen-Chen Hu, 153-178. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-6571-2.ch006

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Abstract

Compressive Sensing (CS) has been proven effective to elevate some of the problems associated with spectrum sensing in wideband Cognitive Radio (CR) networks through efficient sampling and exploiting the underlying sparse structure of the measured frequency spectrum. In this chapter, the authors discuss the motivation and challenges of utilizing collaborative approaches for compressive spectrum sensing. They survey the different approaches and the key published results in this domain. The authors present in detail an approach that utilizes Kronecker sparsifying bases to exploit the two-dimensional sparse structure in the measured spectrum at different, spatially separated cognitive radios. Simulation results show that the presented scheme can substantially reduce the Mean Square Error (MSE) of the recovered power spectrum density over conventional schemes while maintaining the use of a low-rate Sub-Nyquist Analog to Information Converter. It is also shows that one can achieve dramatically lower MSE under low compression ratios using a dense measurement matrix while using Nyquist rate ADC.

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