Skip to main content
SpringerLink
Log in

Cooperative Spectrum Sensing with Small Sample Size in Cognitive Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Cooperative spectrum sensing based on Dempster–Shafer (D–S) theory has attracted a large amount of interest in cognitive wireless sensor networks. However, most of them employ energy detection (ED) in local sensing, where the classical Gaussian approximation of ED is accurate only with a large number of data samples. In this paper, aiming at drastically reduce the computational cost and the sensing process duration, we consider that a small sample size is collected at each node of the network. In this configuration, to perform the D–S fusion we introduce new basic probability of assignment functions derived from the statistics of the eigenvalues of the samples covariance matrix. To that end, we introduce a relevant approximation of the Tracy–Widom distribution that allows us to cope with the small sample size. Simulation results show that the proposed method allows to improve significantly the detection performance compared to other techniques, even with small number of samples.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Yücek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials, 11(1), 116.

    Article  Google Scholar 

  2. Sahai, A., Hoven, N., & Tandra, R. (2004). Some fundamental limits on cognitive radio. In Citeseer 42th Allerton conference on communication, control, and computing.

  3. Urkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceedings of the IEEE, 55(4), 523.

    Article  Google Scholar 

  4. Enserink, S., & Cochran, D. (1994). A cyclostationary feature detector. In IEEE 28th Asilomar conference on signals, systems and computers, vol. 2, (pp. 806–810).

  5. Gavrilovska, L., & Atanasovski, V. (2011). Spectrum sensing framework for cognitive radio networks. Wireless Personal Communications, 59(3), 447.

    Article  Google Scholar 

  6. Subhedar, M., & Birajdar, G. (2011). Spectrum sensing techniques in cognitive radio networks: A survey. International Journal of Next-Generation Networks, 3(2), 37.

    Article  Google Scholar 

  7. Akan, O. B., Karli, O. B., & Ergul, O. (2009). Cognitive radio sensor networks. IEEE Network, 23(4), 34.

    Article  Google Scholar 

  8. Akyildiz, I. F., Lo, B. F., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication, 4(1), 40.

    Article  Google Scholar 

  9. Qihang, P., Kun, Z., Jun, W., & Shaoqian, L. (2006). A distributed spectrum sensing scheme based on credibility and evidence theory in cognitive radio context. In IEEE 17th international symposium on personal, indoor and mobile radio communications (PIMRC), (pp. 1–5).

  10. Nguyen-Thanh, N., & Koo, I. (2011). Evidence-theory-based cooperative spectrum sensing with efficient quantization method in cognitive radio. IEEE Transactions on Vehicular Technology, 60(1), 185.

    Article  Google Scholar 

  11. Han, Y., Chen, Q., & Wang, J.-X. (2012). An enhanced DS theory cooperative spectrum sensing algorithm against SSDF attack. In IEEE 75th vehicular technology conference (VTC Spring). (pp. 1–5).

  12. Wang, J., Feng, S., Wu, Q., Zheng, X., Xu, Y., & Ding, G. (2014). A robust cooperative spectrum sensing scheme based on dempster–shafer theory and trustworthiness degree calculation in cognitive radio networks. EURASIP Journal on Advances in Signal Processing, 2014(1), 1.

    Article  Google Scholar 

  13. Rugini, L., Banelli, P., & Leus, G. (2013). Small sample size performance of the energy detector. IEEE Communications Letters, 17(9), 1814.

    Article  Google Scholar 

  14. Zeng, Y., & Liang, Y.-C. (2009). Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications, 57(6), 1784.

    Article  Google Scholar 

  15. Taherpour, A., Nasiri-Kenari, M., & Gazor, S. (2010). Multiple antenna spectrum sensing in cognitive radios. IEEE Transactions on Wireless Communications, 9(2), 814.

    Article  Google Scholar 

  16. Lin, F., Qiu, R. C., Hu, Z., Hou, S., Browning, J. P., & Wicks, M. C. (2012). Generalized fmd detection for spectrum sensing under low signal-to-noise ratio. IEEE Communications Letters, 16(5), 604.

    Article  Google Scholar 

  17. Huang, L., Fang, J., Liu, K., & So, H. C. (2015). An eigenvalue-moment-ratio approach to blind spectrum sensing for cognitive radio under sample-starving environment. IEEE Transactions on Vehicular Technology, 64(8), 3465.

    Article  Google Scholar 

  18. Ma, Z., et al. (2012). Accuracy of the Tracy–Widom limits for the extreme eigenvalues in white wishart matrices. Bernoulli, 18(1), 322.

    Article  MathSciNet  MATH  Google Scholar 

  19. Cho, Y. S., Kim, J., Yang, W. Y., & Kang, C. G. (2010). MIMO-OFDM wireless communications with MATLAB. London: Wiley.

    Book  Google Scholar 

  20. Xiao, C., Zheng, Y. R., & Beaulieu, N. C. (2006). Novel sum-of-sinusoids simulation models for rayleigh and rician fading channels. IEEE Transactions on Wireless Communications, 5(12), 3667.

    Article  Google Scholar 

  21. Shafer, G., et al. (1976). A mathematical theory of evidence (Vol. 1). Princeton: Princeton University Press.

    MATH  Google Scholar 

  22. Tulino, A. M., & Verdú, S. (2004). Random matrix theory and wireless communications (Vol. 1). Hanover, MA: Now Publishers Inc.

    MATH  Google Scholar 

  23. Johnstone, I. M. (2001). On the distribution of the largest eigenvalue in principal components analysis. Annals of Statistics, 29(2), 295–327.

    Article  MathSciNet  MATH  Google Scholar 

  24. Tracy, C. A., & Widom, H. (1996). On orthogonal and symplectic matrix ensembles. Communications in Mathematical Physics, 177(3), 727.

    Article  MathSciNet  MATH  Google Scholar 

  25. Dieng, M. (2005). Distribution functions for edge eigenvalues in orthogonal and symplectic ensembles: Painlevé representations. International Mathematics Research Notices, 2005(37), 2263.

    Article  MathSciNet  MATH  Google Scholar 

  26. Garrison, I., Martin, R. K., Sethares, W. A., Hart, B., Chung, W., Balakrishnan, J., et al. (2001). DTV channel characterization. In Proceedings of conference on information sciences and systems.

Download references

Author information

Authors and Affiliations

  1. UBL, UMR CNRS 6164, IETR Laboratory, University of Nantes, Nantes, France

    Shaoyang Men, Pascal Chargé & Sébastien Pillement

Authors
  1. Shaoyang Men
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pascal Chargé
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sébastien Pillement
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaoyang Men.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Men, S., Chargé, P. & Pillement, S. Cooperative Spectrum Sensing with Small Sample Size in Cognitive Wireless Sensor Networks. Wireless Pers Commun 96, 1871–1885 (2017). https://doi.org/10.1007/s11277-017-4273-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-4273-5

Keywords

Search

Navigation