Moment Based Spectrum Sensing Algorithm in Cognitive Radio Network for Future of 5G
Journal of Electrical and Electronic Engineering
Volume 6, Issue 1, February 2018, Pages: 12-21
Received: Dec. 18, 2017; Accepted: Dec. 28, 2017; Published: Jan. 16, 2018
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Mohamed B. El_Mashade, Electrical Engineering Department, Faculty of Engineering, Al_Azhar University, Cairo, Egypt
Ashraf Aboshosha, NCRRT, EAEA, Nasr City, Cairo, Egypt
Ehab A. Hegazy, Electrical Engineering Department, Faculty of Engineering, Al_Azhar University, Cairo, Egypt
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The cognitive radio is a smart wireless communication system that is aware of its adjacent environment and under a certain approach is capable of using the current available spectrum temporarily without interfering with the primary user. Cyclostationary detection, which exploits the periodic property of communication signal statistics, absent in stationary noise, is a natural candidate for this setting. The proposed work compromises simple moment based spectrum sensing algorithm for cognitive radio networks. It is made-up that the transmitted signal samples are binary (quadrature) phase-shift keying BPSK (QPSK), Mary quadrature amplitude modulation (QAM) or continuous uniformly distributed random variables and the noise samples are independent and identically distributed circularly symmetric complex Gaussian random variables all with unknown (inadequate) variance. Based on these assumptions, the proposed work offer a simple test statistics engaging a ratio of second and fourth moments. For this statistics, suggested work will deliver analytical expressions for both probability of false alarm (Pf) and probability of detection (Pd) in an additive white Gaussian noise (AWGN) channel. Here, will approve the theoretical expressions through simulation program. In addition, under noise variance uncertainty, simulation results decide that the suggested moment based detector provides better detection performance compared to that of energy detector in AWGN and Rayleigh fading channels.
Cognitive Radio, Spectrum Sensing, Phase Shift Keying, Probability of Detection
To cite this article
Mohamed B. El_Mashade, Ashraf Aboshosha, Ehab A. Hegazy, Moment Based Spectrum Sensing Algorithm in Cognitive Radio Network for Future of 5G, Journal of Electrical and Electronic Engineering. Vol. 6, No. 1, 2018, pp. 12-21. doi: 10.11648/j.jeee.20180601.12
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This article 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.
I. F. Akyildiz, W. Lee, M. C. Vuran, and S. Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey,” Journal of Computer Networks (ELSEVIER), vol. 50, pp. 2127–2159, 2006.
M. Nekovee, “A survey of cognitive radio access to TV white spaces, “International Journal of Digital Multimedia Broadcasting, pp. 1–11, 2010.
D. Cabric, S. M. Mishra, and R. W. Brodersen, “Implementation issues in spectrum sensing for cognitive radios,” in the 38th Asilomar Conference on Signals, Systems and Computers 2004 (Asilomar), Pacific Grove, CA, USA, 7–10 Nov. 2004, pp. 772–776.
R. Tandra and A. Sahai, “SNR walls for signal detection,” IEEE J. Sel. Topics Sig. Proc., vol. 2, pp. 4–17, Feb. 2008.
A. Tkachenko, A. D. Cabric, and R. W. Brodersen, “Cyclostationary feature detector experiments using reconfigurable BEE2,” in 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks 2007 (DySPAN), 17–20 Apr. 2007, pp. 216–219.
T. E. Bogale and L. Vandendorpe, “Multi-cycle cyclostationary based spectrum sensing algorithm for OFDM signals with noise uncertainty in cognitive radio networks,” in Military Communications Conference (MILCOM), Orlando, FL, USA, 29 Oct.–01 Nov. 2012.
Y. Zeng and Y. Liang, “Robustness of the cyclostationary detection to cyclic frequency mismatch,” in IEEE Personal Indoor and Mobile Radio Communications (PIMRC), Istanbul, Turkey, 26–30 Sep. 2010, pp. 2704–2709.
Y. Zeng and Y-c. Liang, “Eigenvalue-based spectrum sensing algorithms for cognitive radio,” IEEE Tran. Comm., vol. 57, no. 6, pp. 1784–1793, Jun. 2009.
M. Wylie-Green, “Dynamic spectrum sensing by multiband OFDM radio for interference mitigation,” in First IEEE International Symposium on DySPAN 2005, 2005, pp. 619–625.
T. Weiss, J. Hillenbrand, and F. Jondral, “A diversity approach for the detection of idle spectral resources in spectrum pooling systems,” in Proc. of the 48th Int. Scientific Colloquium, Ilmenau, Germany, Sep. 2003.
A. Papoulis and S. U. Pillai, Probability, Random Variables and Stochastic Processes, McGraw-Hill, 2002.
R. J. Serfling, Approximation Theorems of Mathematical Statistics, John Wiley and Sons, 1980.
M. R. Morelande and A. M. Zoubir, “Detection of phase modulated signals in additive noise,” IEEE Sig. Proc. Lett., vol. 8, no. 7, pp. 199–202, Jul. 2001.
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