Cyclostationary Feature Detection Based Spectrum Sensing Technique of Cognitive Radio in Nakagami-m Fading Environment

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)


The main function of Cognitive Radio network is to sense the spectrum band to check whether the primary user is present or not in a given spectrum band at a place. One of the most efficient ways of spectrum sensing technique is cyclostationary feature detection. Though the computational complexity is very high in case of cyclostationary feature detection, still it is very effective in case of unknown level of noise. Here, spectral correlation function (SCF) of the received signal is determined. In this paper, SCF is calculated using Hanning, Hamming and Kaiser windows and also we have determined the cyclic periodogram of the input signal. In each case, we have considered Nakagami-m channel fading. Likelihood ratio test has been performed by varying the parameters of the channel fading distribution. Finally, the effects of the windows have been studied in the numerical section.


Cognitive radio (CR) Cyclostationary feature detection Likelihood ratio test Nakagami-m fading Spectral correlation function (SCF) 


  1. 1.
    Haykin, S.: Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)CrossRefGoogle Scholar
  2. 2.
    Federal Communications Commission (FCC): In the Matter of Facilitating Opportunities for Flexible, Efficient and Reliable Spectrum Use Employing Cognitive Radio Technologies. ET Docket No. 03-108 (2003)Google Scholar
  3. 3.
    Akyildiz, I.F., Lee, W.Y., Mohanty, S.: Next generation dynamic spectrum access/cognitive radio wireless networks. Comput. Netw. 50, 2127–2159 (2006)CrossRefMATHGoogle Scholar
  4. 4.
    Cabric, D., Mishra, S.M., Willkomm, D., Brodersen, R.W., Wolisz, A.: A cognitive radio approach for usage of virtual unlicensed spectrum. In: Proceedings of 14th IST Mobile and Wireless Communications Summit (2005)Google Scholar
  5. 5.
    Ghesami, A., Sousa, E.S.: Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs. IEEE Commun. Mag. 32–39 (2008)Google Scholar
  6. 6.
    Gardner, W.A.: Signal interception: a unifying theoretical framework for feature detection. IEEE Trans. Commun. 36(8) (1988)Google Scholar
  7. 7.
    Cochran, D., Enserink, S.: A cyclostationary feature detector. In: 28th Asilomar Conference (1994)Google Scholar
  8. 8.
    Ustok, R.F.: Spectrum sensing techniques for cognitive radio systems with multiple antennas. MS thesis, Electronics and Communication Engineering, IZMIR Institute of Technology (2010)Google Scholar
  9. 9.
    Tkachenko, A., Cabric, D., Brodersen, R.W.: Cyclostationary feature detector experiments using reconfigurable BEE2. In: Proceeding of New Frontiers in Dynamic Spectrum Access Networks. DySPAN (2007)Google Scholar
  10. 10.
    Sutton, P.D., Nolan, K.E., Doyle, L.E.: Cyclostationary signatures in practical cognitive radio applications. IEEE J. Sel. Areas Commun. 26(1), 13–24 (2008)CrossRefGoogle Scholar
  11. 11.
    Turunen, V., Kosunen, M., Huttunen, A., Kallioinen, S., Ikonen, P., Parssinen, A., Ryynanen, J.: Implementation of cyclostationary feature detector for cognitive radios. In: Proceeding of Cognitive Radio Oriented Wireless Networks and Communications, CROWNCOM (2009)Google Scholar
  12. 12.
    Choi, K.W., Jeon, W.S., Jeong, D.G.: Sequential detection of cyclostationary signal for cognitive radio. IEEE Trans. Wirel. Commun. 8(9) (2009)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.RCC Institute of Information TechnologyKolkataIndia

Personalised recommendations