Advertisement

An Improved Cyclostationary Feature Detection Algorithm

  • Tianfeng Yan
  • Fuxin XuEmail author
  • Nan Wei
  • Zhifei Yang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

For the question of the limits for practical application in the actual problems about complicated calculation and time-consuming of cyclostationary feature detection in the spectrum detection process, this paper proposes a new improvement algorithm based on the spectrum correlation characteristics and test statistics of the cyclostationary detection, and deduced its detection probability and false alarm probability. The improved algorithm starts from the spectral correlation characteristics and proves that the cyclic spectrum is conjugate symmetry about the relevant axis, which reduces the computational complexity. Starting from the test statistics, after discarding the more complex correlation factors, the computation complexity and detection performance of the improved test statistics are analyzed. It is concluded that the complexity is obviously reduced and the performance loss is minimal after discarding the correlation factors. The simulation results show that the improved algorithm detection performance is slightly reduced, but the computational complexity is greatly reduced, which further satisfies the requirement of fast and accurate spectrum detection and has strong practicality.

Keywords

Spectrum correlation Test statistics Computational complexity Cyclostationary feature detection 

Notes

Acknowledgement

This study is supported by Scientific research plan projects of Gansu Natural Science Foundation (1508RJZA071); Lanzhou Jiaotong University Youth Fund (2015008).

References

  1. 1.
    Liu, X.L., Zhu, Q.: Joint frequency spectrum detection method based on energy-cycling stationarity feature. J. Nanjing Univ. Posts Telecommun. 30(3), 34–38 (2010)Google Scholar
  2. 2.
    Yuan, H.Y., Hu, Y.: Spectrum sensing algorithm combining cyclostationarity and adaptive dual threshold detection. J. Comput. Aided Des. Comput. Graph. 25(4), 573–577 (2013)Google Scholar
  3. 3.
    Ma, B., Fang, Y., Xie, X.Z.: An improved cyclostationary feature detection algorithm for master user at random arrival. J. Electron. Inf. Technol. 37(7), 1531–1537 (2015)Google Scholar
  4. 4.
    Derakhshani, M., Le-Ngoc, T., Nasiri-Kenari, M.: Efficient cooperative cyclostationary spectrum sensing in cognitive radios at low snr regimes. IEEE Trans. Wirel. Commun. 10(11), 3754–3764 (2011)CrossRefGoogle Scholar
  5. 5.
    Chen, X.Y., Yang, R.J., Li, X.B., Xie, C.: Collaborative detection of cyclo-stationary spectrum based on maximum ratio merging. Mod. Def. Technol. 41(4), 113–117 (2013)Google Scholar
  6. 6.
    Yang, M., Li, Y., Liu, X., Tang, W.: Cyclostationary feature detection based spectral sensing algorithm under complicated electromagnetic environment in cognitive radio networks. China Commun. 12(9), 35–44 (2015). (English)CrossRefGoogle Scholar
  7. 7.
    Lu, G.Y., Ye, Y.H., Sun, Y., Mi, Y.: Spectrum-aware algorithm based on goodness-of-fit test for overcoming noise uncertainty. Telecommun. Technol. 56(1), 26–31 (2016)Google Scholar
  8. 8.
    Gao, Y.L., Chen, Y.P., Guan, X., Zhang, Z.Z., Sha, X.J.: Spectrum sensing algorithm based on cyclic spectrum symmetry. In: National Cognitive Wireless Network Conference (2011)Google Scholar
  9. 9.
    Zhang, J., Zhang, L., Huang, H., Zhang, X.J.: Improved cyclostationary feature detection based on correlation between the signal and noise. In: International Symposium on Communications and Information Technologies, pp. 611–614. IEEE (2016)Google Scholar
  10. 10.
    Trees, H.L.V.: Detection, estimation, and modulation theory. Part III. A Papoulis Probability Random Variables & Stochastic Processes, vol. 8(10), pp. 293–303 (2001)Google Scholar
  11. 11.
    Qi, P.H., Si, J.B., Li, Z., Gao, R.: A novel spectrum-aware spectrum sensing algorithm for anti-noise uncertainty spectrum. J. Xidian Univ. 40(6), 19–24 (2013)Google Scholar
  12. 12.
    Zhu, Y.: Research on Key Technologies for Efficient Spectrum Detection in Cognitive Wireless Networks. Beijing University of Posts and Telecommunications (2014)Google Scholar
  13. 13.
    Wang, H., Yuan, X.B., Zhang, H.L.: Statistical analysis based on energy detection method. Aerosp. Electron. Warf. 30(6), 41–44 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringLanzhou Jiaotong UniversityLanzhouChina

Personalised recommendations