Improved Spectrum Sensing Method for Cognitive Radio Based on Time Domain Averaging and Correlation

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)


Based on the combination of time domain averaging and correlation, we propose an effective time domain averaging and correlation based spectrum sensing (TDA-C-SS) method used in very low SNR environments. With the assumption that the received signal samples from the primary users are deterministic, the proposed TDA-C-SS method processes the received samples by a time averaging operation to improve the SNR. Correlation operation is then performed with a correlation matrix to determine the existence of the primary signal in the received samples. The TDA-C-SS method does not need any prior information on the received samples and the associated noise power to achieve improved sensing performance. Simulation results are presented to show the effectiveness of the proposed TDA-C-SS method.


Cognitive radio Spectrum sensing Time domain averaging Correlation 


  1. 1.
    Cabric D, Mishra SM, Brodersen RW (2004) Implementation issues in spectrum sensing for cognitive radios. In: Proc. Asilomar conf. signals, syst. computers, Pacific Grove, CA, Nov 2004Google Scholar
  2. 2.
    Sahai A, Cabric D (2005) Spectrum sensing: fundamental limits and practical challenges. A tutorial in IEEE int. symp. New Frontiers DySPAN, Baltimore, MD, Nov 2005Google Scholar
  3. 3.
    Cabric D, Tkachenko A, Brodersen RW (2006) Spectrum sensing measurements of pilot, energy, and collaborative detection. In: Proc. military commun. conf. (MILCOM), Washington, DC, Oct 2006Google Scholar
  4. 4.
    Chen H-S, Gao W, Daut DG (2007) Signature based spectrum sensing algorithms for IEEE 802.22 WRAN. In: Proc. IEEE int. conf. communications (ICC), Jun 2007Google Scholar
  5. 5.
    Han N, Shon SH, Joo JO, Kim JM (2006) Spectral correlation based signal detection method for spectrum sensing in IEEE 802.22 WRAN systems. In: Proc. intern. conf. advanced commun. technology, Korea, Feb 2006Google Scholar
  6. 6.
    Zeng Y, Liang Y-C (2009) Spectrum-sensing algorithms for cognitive radio based on statistical covariance. IEEE Trans Veh Technol 58(4):1804–1815CrossRefGoogle Scholar
  7. 7.
    Zeng Y, Liang Y-C (2009) Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Trans Commun 57(6):1784–1793CrossRefGoogle Scholar
  8. 8.
    Zeng Y, Liang Y-C, Zhang R (2008) Blindly combined energy detection for spectrum sensing in cognitive radio. IEEE Signal Process Lett 15:649–652CrossRefGoogle Scholar
  9. 9.
    Xu J-Y, Zhang H-S (2009) Detection to primary user based on radio frequency fingerprint in cognitive radio. In: Proc. intern. cong. image and signal processing (CISP), P.R. China, Oct 2009Google Scholar
  10. 10.
    Lecklider T (2010) Raising the B/W Bar to 32 GHz. Eval Eng 49(6):34Google Scholar
  11. 11.
    Zhao YJ, Hu YH, Wang HJ (2012) Enhanced random equivalent sampling based on compressed sensing. IEEE Trans Instrum Meas 61(3):579–586CrossRefGoogle Scholar
  12. 12.
    López-Valcarce R, Vazquez-Vilar G (2009) Wideband spectrum sensing in cognitive radio: joint estimation of noise variance and multiple signal levels. 2009 I.E. international workshop on signal processing advances for wireless communications (SPAWC 2009), Italy, Jun 2009Google Scholar
  13. 13.
    Pini M, Akos D-M (2007) Exploiting GNSS signal structure to enhance observability. IEEE Trans Aerospace Electron Sys 43(4):1553–1565CrossRefGoogle Scholar
  14. 14.
    Zeng Y, Liang Y-C (2006) Performance of Eigenvalue based sensing algorithms for detection of DTV and wireless microphone signals. IEEE 802.22-06/186r0, Sept 2006Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Information Engineering School of EEENanyang Technological UniversitySingaporeSingapore

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