Skip to main content

Advertisement

Springer Nature Link
Log in

Reducing the Sampling Complexity of Energy Detection in Cognitive Radio Networks under Low SNR by Using the Optimal Stochastic Resonance Technique

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

We propose a novel noncooperative technique in cognitive radio (CR) networks, which is based on the optimal stochastic resonance (SR) technique. By introducing the dynamic system approach of SR into the noncooperative spectrum sensing process, the defect of high sampling complexity of traditional energy detector can be reduced efficiently and thus can guarantee the applicability of the optimal SR-based energy detection method. The optimization of the signal-to-noise ratio (SNR) improvement of the system ensures the lowest sampling complexity needed to reach certain performance requirement. Computer simulations show that it can reduce the sampling complexity compared with traditional energy detector used in IEEE 802.22 draft especially under low SNR environments. It can certainly be extended to other wide application areas.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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. I.F. Akyildiz, W.Y. Lee, M.C. Vuran, S. Mohanty, NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput. Netw. 50(13), 2127–2159 (2006)

    Article  MATH  Google Scholar 

  2. V.S. Anishchenko, V.V. Astakhov, A.B. Neiman, T.E. Vadivasova, L. Schimansky-Geier, Nonlinear Dynamics of Chaotic and Stochastic Systems: Tutorial and Modern Developments (Spring, Berlin, 2002)

    Google Scholar 

  3. H. Chen, P.K. Varshney, Theory of the stochastic resonance effect in signal detection: part II – variable detectors. IEEE Trans. Signal Process. 56(10), 5031–5041 (2008)

    Article  MathSciNet  Google Scholar 

  4. H. Chen, P.K. Varshney, S.M. Kay, J.H. Michels, Theory of the stochastic resonance effect in signal detection: part I – fixed detectors. IEEE Trans. Signal Process. 55(7), 3172–3184 (2007)

    Article  MathSciNet  Google Scholar 

  5. C. Cordeiro, K. Challapali, D. Birru, IEEE 802.22: An introduction to the first wireless standard based on cognitive radios. J. Commun. 1(1), 38–47 (2006)

    Google Scholar 

  6. A.V. Dandawate, G.B. Giannakis, Statistical tests for presence of cyclostationarity. IEEE Trans. Signal Process. 42(9), 2355–2369 (1994)

    Article  Google Scholar 

  7. A.K. Dhara, Enhancement of signal-to-noise ratio. J. Stat. Phys. 87(1–2), 251–271 (1997)

    Article  MATH  Google Scholar 

  8. F.F. Digham, M.S. Alouini, M.K. Simon, On the energy detection of unknown signals over fading channels. IEEE Trans. Commun. 55(1), 21–24 (2007)

    Article  Google Scholar 

  9. S. Haykin, Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)

    Article  Google Scholar 

  10. D. He, Y.P. Lin, C. He, L.G. Jiang, A novel spectrum sensing technique in cognitive radio based on stochastic resonance. IEEE Trans. Veh. Technol. 59(4), 1680–1688 (2010)

    Article  Google Scholar 

  11. M. Kay, Fundamentals of Statistical Signal Processing, vol. II: Detection Theory (Prentice Hall, Upper Saddle Rover, 1998)

    Google Scholar 

  12. S. Kay, J.H. Michels, H. Chen, P.K. Varshney, Reducing probability of decision error using stochastic resonance. IEEE Signal Process. Lett. 13(11), 695–698 (2006)

    Article  Google Scholar 

  13. B. McNamara, K. Wilesenfeld, Theory of stochastic resonance. Phys. Rev. A 39(9), 4854–4869 (1989)

    Article  Google Scholar 

  14. S. Mitaim, B. Kosko, Adaptive stochastic resonance. Proc. IEEE 86(11), 2152–2183 (1998)

    Article  Google Scholar 

  15. M.S. Oude Alink, A.B.J. Kokkeler, E.A.M. Klumperink, G.J.M. Smit, B. Nauta, Lowering the SNR wall for energy detection using cross-correlation. IEEE Trans. Veh. Technol. 60(8), 3748–3757 (2011)

    Article  Google Scholar 

  16. A. Taherpour, M. Nasiri-Kenari, S. Gazor, Multiple antenna spectrum sensing in cognitive radios. IEEE Trans. Wirel. Commun. 9(2), 814–823 (2010)

    Article  Google Scholar 

  17. R. Tandra, A. Sahai, SNR walls for signal detection. IEEE J. Sel. Top. Signal Process. 2(1), 4–17 (2008)

    Article  Google Scholar 

  18. P. Wang, J. Fang, N. Han, H. Li, Multiantenna-assisted spectrum sensing for cognitive radio. IEEE Trans. Veh. Technol. 59(4), 1791–1800 (2010)

    Article  Google Scholar 

  19. Y. Zeng, Y.C. Liang, Spectrum-sensing algorithms for cognitive radio based on statistical covariances. IEEE Trans. Veh. Technol. 58(4), 1804–1815 (2009)

    Article  Google Scholar 

  20. R. Zhang, T. Lim, Y.C. Liang, Y. Zeng, Multi-antenna based spectrum sensing for cognitive radios: a GLRT approach. IEEE Trans. Commun. 58(1), 84–88 (2010)

    Article  Google Scholar 

  21. DVB BlueBook A092r3, Implementation guidelines for DVB handheld services. April 2009. http://www.dvb.org/technology/standards/a092r3.dTR102377.V1.4.1.DVB-H_impl_guide.pdf

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 60802058 and 60832009, the Important National Science and Technology Specific Project of China under Grant No. 2013ZX03001028-005, the National High Technology Research and Development Program of China under Grant No. SS2013AA010702, the ZTE Corporation and University Joint Research Project under Grant No. One1111150008, and the SMC young teacher sponsorship of Shanghai Jiao Tong University.

Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China

    Di He

Authors
  1. Di He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Di He.

Rights and permissions

Reprints and permissions

About this article

Cite this article

He, D. Reducing the Sampling Complexity of Energy Detection in Cognitive Radio Networks under Low SNR by Using the Optimal Stochastic Resonance Technique. Circuits Syst Signal Process 32, 1891–1905 (2013). https://doi.org/10.1007/s00034-013-9552-0

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-013-9552-0

Keywords

Search

Navigation