Cognitive Radio Spectrum Sensing Based on Wavelet Denoising

  • Yu Zhao
  • Bin Guo
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 129)


Spectrum sensing which detects the presence of primary user in a licensed spectrum is a fundamental problem in cognitive radio. Sensing accuracy is the most important factor to determine the performance of cognitive radio. Due to the existence of noise, many algorithms are subject to some limitations for application. In this paper, a novel spectrum sensing method is proposed based on the wavelet denoising. An important scenario for the different characterization of signal and noise in the wavelet multi-scale analysis is the case where the receiver is able to distinguish the signal and noise. This method does not require any prior information about the primary signal, and also has a good spectrum sensing performance at a low SNR.


Cognitive Radio Primary User Cognitive Radio User Wavelet Shrinkage Wavelet Denoising 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Haykin, S., Life, F.: Cognitive Radio: Brain-Empowered Wireless Communications. IEEE Journal on Selected Areas In Communications 23(2) (February 2005)Google Scholar
  2. 2.
    Taswell, C.: The What, How, and Why of Wavelet Shrinkage Denoising, Computational Toolsmiths, Stanford, CA 94309–9925Google Scholar
  3. 3.
    Ma, J., Li, G.Y.: Signal Processing in Cognitive Radio, vol. 0018-9219/ IEEE (5) (May 2009)Google Scholar
  4. 4.
    Urkowitz, H.: Energy Detection of Unknown Deterministic Signals. Proceedings of the IEEE 55(4) (April 1967)Google Scholar
  5. 5.
    Kang, B.-J.: Spectrum Sensing Issues in Cognitive RadioNetworks, 978-1-4244-4522-6/09/2009 IEEEGoogle Scholar
  6. 6.
    SMallat: A Wavelet Tour of Signal Processing, 2 edn., ISBN 7-111-10159-6Google Scholar
  7. 7.
    Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans.on IT 41(3), 613–627 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Donoho, D.L., Johnstone, I.M., Kerkyacharian, G., Picard, D.: Density Estimation By Wavelet Thresholding. The Annals of Statistics 24(2), 508–539 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Donoho, D.L., Johnstone, I.: Ideal spatial adaptation via wavelet shrinkage. Biometrika 81, 425–455 (1994)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Tony Cai, T., Brown, L.D.: Wavelet Shrinkage For Nonequispaced Samples. The Annals of Statistics 26(5), 1783–1799 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Xu, Y., Weaver, J.B., Healy Jr., D.M., Lu, J.: Wavelet Transform Domain Filters: A Spatially Selective Noise Filtration Technique. IEEE Transactions on Image Processing 3(6), 747 (1994)CrossRefGoogle Scholar
  12. 12.
    Boyer, K.G.: ” The Fast Wavelet Transform (FWT)” A thesis submitted to the University of Colorado at Denver in partial fulfillment of the requirement for the degree of master of Science Applied MathematicsGoogle Scholar
  13. 13.
    Sheikh, F., Masud, S., Bing, B.: Harmonic power detection in wideband cognitive radios. IET Signal Process. 3(1), 40–50 (2009), doi:10.1049/iet-spr:20070221CrossRefGoogle Scholar
  14. 14.
    Ghasemi, A., Sousa, E.S.:  Collaborative Spectrum Sensing for Opportunistic, Access in Fading Environments. IEEE, 10.1109/DYSPAN.2005.1542627 Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Changchun University of Science and TechnologyChangchunChina

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