Skip to main content
SpringerLink
Log in

Intelligent selection of threshold in covariance-based spectrum sensing for cognitive radio networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

The radio spectrum sensing has been an important issue of research in cognitive radio networks over the last decade and the appropriate selection of threshold plays a crucial role in the process of spectrum sensing. The conventional channel sensing methods generally employ a fixed threshold, which is either based on the principle of constant false alarm rate (CFAR) or the principle of constant detection rate (CDR). The sensing performance of these schemes degrades under low signal to noise ratio (SNR) and noise uncertainty. The problem of noise uncertainty occurring in energy detection (ED) based spectrum sensing method can be overcome by using covariance-based spectrum sensing scheme. However, the performance of covariance based spectrum sensing degrades at low SNR. This paper proposes a covariance-based channel sensing method, where the adaptive threshold is selected in an intelligent manner to minimize the probability of error with sufficient protection to primary user (PU). First, an adaptive threshold is derived by considering both probability of detection and probability of false alarm such that the total decision error probability is minimized. This adaptive threshold is then considered along with two other thresholds based on CFAR and CDR schemes, for the final selection of threshold such that the protection to PU is maximized. The proposed approach also provides the minimum number of samples required for reliable spectrum sensing. As shown by the simulation results, the proposed scheme exhibits better detection performance compared to ED based schemes as well as the existing covariance-based detection method in terms of probability of detection and probability of decision error.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Federal Communications Commission. (2002) Spectrum policy task force report, FCC 02–155, Nov 2002.

  2. FCC. (2003) Facilitating opportunities for flexible efficient and reliable spectrum use employing cognitive radio technologies, notice of proposed rule making and order, in FCC03–322, Dec 2003.

  3. Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6, 13–18.

    Article  Google Scholar 

  4. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23, 201–220.

    Article  Google Scholar 

  5. IEEE 802.22 Wireless RAN. (2005) Functional requirements for the 802.22 WRAN standard, IEEE 802.22-05/0007r46, Oct 2005.

  6. Key, S. M. (1998). Fundamentals of statistical signal processing: Detection theory (Vol. 2). Upper saddle: Prentice Hall.

    Google Scholar 

  7. Atapattu, S., Tellambura, C., Jiang, H., & Rajatheva, N. (2015). Unified analysis of low-SNR energy detection and threshold selection. IEEE Transactions on in Vehicular Technology, 64(11), 5006–5019.

    Article  Google Scholar 

  8. Sobron, I., Diniz, P. S. R., Martins, W. A., & Velez, M. (2015). Energy detection technique for adaptive spectrum sensing. IEEE Transactions on Communications, 63(3), 617–627.

    Article  Google Scholar 

  9. Wang, N., Gao, Y., & Zhang, X. (2013). Adaptive spectrum sensing algorithm under different primary user utilizations. IEEE Communications Letters, 17(9), 1838–1841.

    Article  Google Scholar 

  10. Vladeanu, C., Nastase, C. V., & Martian, A. (2016). Energy detection algorithm for spectrum sensing using three consecutive sensing events. IEEE Wireless Communications Letters, 5(3), 284–287.

    Article  Google Scholar 

  11. Kozal, A. S. B., Merabti, M., & Bouhafs, F. (2012) An improved energy detection scheme for cognitive radio networks in low SNR region. IEEE Symposium on Computers and Communications (ISCC), Cappadocia. (pp. 000684–000689).

  12. Chen, H.-S., Ga., W. & Daut, D. G. (2007) Signature based spectrum sensing algorithms for IEEE 802.22 WRAN. In IEEE International Conference Communications (ICC).

  13. Zhang, X., Gao, F., Chai, R., & Jiang, T. (2015). Matched filter based spectrum sensing when primary user has multiple power levels. China Communications, 12(2), 21–31.

    Article  Google Scholar 

  14. Oner, M., & Jondral, F. (2004) Cyclostationary-based methods for the extraction of the channel allocation information in a spectrum pooling system. In Proceedings IEEE Radio and Wireless Conference, Atlanta, GA. (pp. 279–282).

  15. Yang, M., Li, Y., Liu, X., & Tang, W. (2015). Cyclostationary feature detection based spectrum sensing algorithm under complicated electromagnetic environment in cognitive radio networks. China Communications, 12(9), 35–44.

    Article  Google Scholar 

  16. Zeng, Y., Koh C. L., & Liang, Y. C. (2008) Maximum eigenvalue detection: Theory and application. In IEEE International Conference on Communications, 2008. ICC ‘08. (pp. 4160–4164, 19–23).

  17. Charan, C., & Pandey, R. (2016). Eigenvalue based double threshold spectrum sensing under noise uncertainty for cognitive radio. Optik—International Journal for Light and Electron Optics, 127(15), 5968–5975.

    Article  Google Scholar 

  18. Zeng, Y., & Liang, Y. C. (2009). Spectrum-sensing algorithms for cognitive radio based on statistical covariances. IEEE Transactions on Vehicular Technology, 58(4), 1804–1815.

    Article  Google Scholar 

  19. Yang, X., Lei, K., Peng, S., & Cao, X. (2011). Blind detection for primary user based on the sample covariance matrix in cognitive radio. IEEE Communications Letters, 15(1), 40–42.

    Article  Google Scholar 

  20. Jin, M., Li, Y., & Ryu, H. G. (2012). On the performance of covariance based spectrum sensing for cognitive radio. IEEE Transactions on Signal Processing, 60, 3670–3682.

    Article  MathSciNet  Google Scholar 

  21. Yang, X., Lei, K., Peng, S., & Cao, X. (2011). Blind detection for primary user based on the sample covariance matrix in cognitive radio. IEEE Communications Letters, 15, 40–42.

    Article  Google Scholar 

  22. Jin, M., Guo, Q., Xi, J., Li, Y., Yu, Y., & Huang, D. (2015). Spectrum sensing using weighted covariance matrix in rayleigh fading channels. IEEE Transactions on Vehicular Technology, 64, 5137–5148.

    Article  Google Scholar 

  23. He., D. (2015) A novel spectrum sensing method in cognitive radio networks based on graph theory. In 2015 IEEE Global Communications Conference (GLOBECOM), San Diego, CA, (pp. 1–6).

  24. Akhtar, F., Rehmani, M. H., & Reisslein, M. (2016). White space: Definitional perspectives and their role in exploiting spectrum opportunities. Telecommunications Policy, 40(4), 319–331.

    Article  Google Scholar 

  25. Monemi, M., Rasti, M., & Hossain, E. (2015) Characterizing feasible interference region for underlay cognitive radio networks, In 2015 IEEE International Conference on Communications (ICC), London, (pp. 7603–7608).

  26. Monemi, M., Rasti, M., & Hossain, E. (2016). On characterization of feasible interference regions in cognitive radio networks. IEEE Transactions on Communications, 64(2), 511–524.

    Article  Google Scholar 

  27. Zhang, W., Wang, C. X., Chen, D., & Xiong, H. (2016). Energy–spectral efficiency tradeoff in cognitive radio networks. IEEE Transactions on Vehicular Technology, 65(4), 2208–2218.

    Article  Google Scholar 

  28. Goldsmith, A., Jafar, S. A., Maric, I., & Srinivasa, S. (2009). Breaking spectrum gridlock with cognitive radios: An information theoretic perspective. Proceedings of the IEEE, 97(5), 894–914.

    Article  Google Scholar 

  29. Chen, Y., & Oh, H. S. (2016). A survey of measurement-based spectrum occupancy modeling for cognitive radios. IEEE Communications Surveys and Tutorials, 18(1), 848–859.

    Article  Google Scholar 

  30. Xing, X., Jing, T., Cheng, W., Huo, Y., & Cheng, X. (2013). Spectrum prediction in cognitive radio networks. IEEE Wireless Communications, 20(2), 90–96.

    Article  Google Scholar 

  31. Saleem, Y., & Rehmani, M. H. (2014). Primary radio user activity models for cognitive radio networks: Survey. Journal of Network and Computer Applications, 43, 1–16.

    Article  Google Scholar 

  32. Höyhtyä, M., et al. (2016). Spectrum occupancy measurements: A survey and use of interference maps. IEEE Communications Surveys and Tutorials, 18(4), 2386–2414.

    Article  Google Scholar 

  33. Tumuluru, V., Wang, P., & Niyato, D. (2010) A neural network based spectrum prediction scheme for cognitive radio. In IEEE International Conference on Communications, 2012, (pp. 1–5).

  34. Ghosh, C., Pagadarai, S., Agrawal, D. P., & Wyglinski, A. M. (2010). A framework for statistical wireless spectrum occupancy modeling. IEEE Transactions on Wireless Communications, 9(1), 38–44.

    Article  Google Scholar 

  35. Sahai. A., & Cabric, D. (2005) Spectrum sensing fundamental limits and practical challenges. In Proceedings IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (dySPAN), (Baltimore, MD).

  36. Tandra, R., & Sahsi, A. (2005) Fundamental limits on detection in low SNR under noise uncertainty. In Wireless Communications 2005, (Maui, HI).

  37. Liang, Y.-C., Zeng, Y., Peh, E., & Hoang, A. T. (2008). Sensing-throughput tradeoff for cognitive radio networks. IEEE Transactions on Wireless Communications, 7, 1326–1337.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, India

    Chhagan Charan & Rajoo Pandey

Authors
  1. Chhagan Charan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajoo Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chhagan Charan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Charan, C., Pandey, R. Intelligent selection of threshold in covariance-based spectrum sensing for cognitive radio networks. Wireless Netw 24, 3267–3279 (2018). https://doi.org/10.1007/s11276-017-1533-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-017-1533-y

Keywords

Search

Navigation