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

Article
  • 133 Downloads

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.

Keywords

Cognitive radio Spectrum sensing Covariance matrices Intelligent selection of threshold Adaptive threshold Low SNR 

References

  1. 1.
    Federal Communications Commission. (2002) Spectrum policy task force report, FCC 02–155, Nov 2002.Google Scholar
  2. 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.Google Scholar
  3. 3.
    Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6, 13–18.CrossRefGoogle Scholar
  4. 4.
    Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23, 201–220.CrossRefGoogle Scholar
  5. 5.
    IEEE 802.22 Wireless RAN. (2005) Functional requirements for the 802.22 WRAN standard, IEEE 802.22-05/0007r46, Oct 2005.Google Scholar
  6. 6.
    Key, S. M. (1998). Fundamentals of statistical signal processing: Detection theory (Vol. 2). Upper saddle: Prentice Hall.Google Scholar
  7. 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.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. 9.
    Wang, N., Gao, Y., & Zhang, X. (2013). Adaptive spectrum sensing algorithm under different primary user utilizations. IEEE Communications Letters, 17(9), 1838–1841.CrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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).Google Scholar
  12. 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).Google Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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).Google Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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).Google Scholar
  17. 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.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.MathSciNetCrossRefGoogle Scholar
  21. 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.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. 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).Google Scholar
  24. 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.CrossRefGoogle Scholar
  25. 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).Google Scholar
  26. 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.CrossRefGoogle Scholar
  27. 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.CrossRefGoogle Scholar
  28. 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.CrossRefGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. 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.CrossRefGoogle Scholar
  31. 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.CrossRefGoogle Scholar
  32. 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.CrossRefGoogle Scholar
  33. 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).Google Scholar
  34. 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.CrossRefGoogle Scholar
  35. 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).Google Scholar
  36. 36.
    Tandra, R., & Sahsi, A. (2005) Fundamental limits on detection in low SNR under noise uncertainty. In Wireless Communications 2005, (Maui, HI).Google Scholar
  37. 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.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNational Institute of TechnologyKurukshetraIndia

Personalised recommendations