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A Kullback-Leiber Divergence-based Spectrum Sensing for Cognitive Radio Systems

무선인지시스템을 위한 Kullback-Leiber Divergence 기반의 스펙트럼 센싱 기법

  • Received : 2011.08.01
  • Accepted : 2011.10.24
  • Published : 2012.02.29

Abstract

In the paper, an information divergence called Kullback-Leiber divergence, which measures the average of the logarithmic difference between two probability density functions, is utilized to derive a novel method for spectrum sensing in cognitive radio systems. In the proposed sensing method, we test whether the observed samples are drawn from the noise distribution by using Kullback-Leiber divergence. It is shown by numerical results that under the same conditions, the proposed Kullback-Leiber divergence-based spectrum sensing always outperforms the energy detection based spectrum sensing significantly, especially in low SNR regime and in fading circumstance.

본 논문에서는 무선인지시스템에서 효율적으로 스펙트럼 센싱을 수행하기 위해, 확률 분포 사이의 대수 차를 측정하는 Kullback-Leiber divergence기반의 새로운 스펙트럼 센싱 기술을 제안한다. 제안된 센싱 기법은 특정 센싱 구간에서의 국부 센싱 측정값들이 잡음 분포에서 발생하였는지, 기사용자 신호에서 발생하였는지를 Kullback-Leiber divergenc를 이용하여 판단한다. 시뮬레이션 을 통해, 제안된 Kullback-Leiber divergence기반의 스펙트럼 센싱 기법이 동일 조건에서 에너지 검출 기반의 스펙트럼 센싱 기법보다 더 좋은 성능을 제공할 수 있음을 보였다. 특히, 페이딩 환경 및 기사용자 신호의 SNR값이 낮은 경우에 에너지 검출 기반의 스펙트럼센싱 기법과 비교할 때 제안된 기법의 성능이 크게 향상됨을 보였다.

Keywords

References

  1. S. Haykin, "Cognitive radio: brain-empowered wireless communications," IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, 2005. https://doi.org/10.1109/JSAC.2004.839380
  2. D. Cabric, A. Tkachenko, and R. W. Brodersen, "Spectrum sensing measurement of pilot, energy, and collaborative detection," Proc. IEEE Military Communications Conf., pp. 1-7, Washington, DC, Oct. 2006.
  3. H. S. Chen, W. Gao, and D. G. Daut, "Signature based spectrum sensing algorithm for IEEE 802.22 WRAN," Proc. IEEE Int. Conf. on Communications (ICC), pp. 6487-6492, Glasgow, Scotland, June 2007.
  4. M. Oner and F. Jondral, "Cyclostationarity based air interface recognition for software radio systems," Proc. IEEE Radio and Wireless Conf., pp. 263-266, Atlanta, GA, Sept. 2004.
  5. M. Oner and F. Jondral, "A cyclostationarity feature detection," Proc. Asilomar Conference on Signals, Systems, and Computer, pp. 806-810, CA, 1994.
  6. H. Urkowitz, "Energy detection of unknown deterministic signals," Proceedings of the IEEE, vol.55, no.4, pp. 523-531, April 1967. https://doi.org/10.1109/PROC.1967.5573
  7. V. I. Kostylev, "Energy detection of a signal with random amplitude," Proceedings of the IEEE Conference on Communications, Newyork, pp. 1606-1610, May 2002.
  8. F. Digham, M.-S. Alouini, and M. K. Simon, "On the energy detection of unknown signals over fading channels," IEEE Trans. Commun., vol. 55, no. 1, pp. 21-24, 2007. https://doi.org/10.1109/TCOMM.2006.887483
  9. Z. Ye, G. Memik and J. Grosspietsch, "Energy detection using estimated noise variance for spectrum sensing in cognitive radio networks," Proc. IEEE wireless communications and networking Conf. (WCNC), pp. 711-716, Nevada, USA, April 2008.
  10. S. Kullback, Information theory and statistics, Dover Publications Inc., Mineola, New York, 1968.
  11. T. M. Cover and J. A. Thomas, Elements of information theory, Wiley series in telecommunications, Jonh Wiley & Son, 1991.
  12. J. R. Hershey and P. A. Olsen, "Approximating the Kullback-Leiber divergence between Gaussian mixture models," Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Vol. 04, pp. 317-320, Hawaii, USA, May 2007.