Compressed Measurements Based Spectrum Sensing for Wideband Cognitive Radio SystemsReport as inadecuate

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International Journal of Antennas and Propagation - Volume 2015 2015, Article ID 654958, 7 pages -

Research Article

Department of Electrical Engineering, University of Tabuk, Tabuk 71491, Saudi Arabia

Department of Electrical Engineering, Assiut University, Assiut 71516, Egypt

Electrical Engineering Department, King Khalid University, Abha 62529, Saudi Arabia

Received 4 August 2015; Revised 8 December 2015; Accepted 10 December 2015

Academic Editor: Stefano Selleri

Copyright © 2015 Taha A. Khalaf et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Spectrum sensing is the most important component in the cognitive radio CR technology. Spectrum sensing has considerable technical challenges, especially in wideband systems where higher sampling rates are required which increases the complexity and the power consumption of the hardware circuits. Compressive sensing CS is successfully deployed to solve this problem. Although CS solves the higher sampling rate problem, it does not reduce complexity to a large extent. Spectrum sensing via CS technique is performed in three steps: sensing compressed measurements, reconstructing the Nyquist rate signal, and performing spectrum sensing on the reconstructed signal. Compressed detectors perform spectrum sensing from the compressed measurements skipping the reconstruction step which is the most complex step in CS. In this paper, we propose a novel compressed detector using energy detection technique on compressed measurements sensed by the discrete cosine transform DCT matrix. The proposed algorithm not only reduces the computational complexity but also provides a better performance than the traditional energy detector and the traditional compressed detector in terms of the receiver operating characteristics. We also derive closed form expressions for the false alarm and detection probabilities. Numerical results show that the analytical expressions coincide with the exact probabilities obtained from simulations.

Author: Taha A. Khalaf, Mohammed Y. Abdelsadek, and Mohammed Farrag



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