The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Distance Measure for Biased Probability Density Functions and Related Equalizer Algorithms for Non-Gaussian Noise

편이 확률밀도함수 사이의 거리측정 기준과 비 가우시안 잡음 환경을 위한 등화 알고리듬

  • 김남용 (강원대학교 전자정보통신공학부)
  • Received : 2012.09.16
  • Accepted : 2012.11.27
  • Published : 2012.12.28
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Abstract

In this paper, a new distance measure for biased PDFs is proposed and a related equalizer algorithm is also derived for supervised adaptive equalization for multipath channels with impulsive and time-varying DC bias noise. From the simulation results in the non-Gaussian noise environments, the proposed algorithm has proven not only robust to impulsive noise but also to have the capability of cancelling time-varying DC bias noise effectively.

이 논문에서는 편이된 확률밀도함수 간 거리 측정이라는 새로운 거리 측정 기준을 제안하고 이에 관련된 등화 알고리듬을 도출하여 충격성 잡음과 시변 직류 잡음이 있는 다경로 채널에 적용하였다. 이러한 비 가우시안 잡음 환경에서 시행한 시뮬레이션의 결과로부터, 제안한 알고리듬이 충격성 잡음에 강인성을 보일 뿐 아니라 시변 직류 잡음도 제거하는 탁월한 능력을 가짐을 입증하였다.

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References

  1. L. M. Garth, "A dynamic convergence analysis of blind equalization algorithms," IEEE Trans. on Commun., vol. 49, pp. 624-634. Apr. 2001. https://doi.org/10.1109/26.917769
  2. K. Blackard, T. Rappaport, and C. Bostian, "Measurements and models of radio frequency impulsive noise for indoor wireless communications," IEEE J. Select. Areas Commun., vol. 11, pp. 991-1001, Sept. 1993. https://doi.org/10.1109/49.233212
  3. K. Koike and H. Ogiwara, "Application of Turbo TCM codes for impulsive noise channel," IEICE Trans. Fund. Electr., vol. E81-A, no. 10, pp. 2032-2039, Oct. 1998.
  4. M. Button, J. Gardiner, and I. Glover, "Measurement of the impulsive noise environment for satellite-mobile radio systems at 1.5 GHz," IEEE Trans. Veh. Technol.. vol. 51, no. 3, pp. 551-560, May 2002. https://doi.org/10.1109/TVT.2002.1002503
  5. M. Richharia, Satellite communication systems: design principles, 2nd Ed. Palgrave Macmillan Limited, 1999.
  6. H. Sedarat, and K. Fishera, "Multicarrier communication in presence of biased-Gaussian noise sources," J. Signal Processing, vol. 88, issue 7, pp. 1627-1635, Jul. 2008. https://doi.org/10.1016/j.sigpro.2007.11.025
  7. J. Mazo, "Asymptotic distortion spectrum of clipped, dc-biased, Gaussian noise," IEEE Trans. Commun., vol. 40, no. 8, pp. 1339-1344, Aug. 1992. https://doi.org/10.1109/26.156638
  8. J. Principe, D. Xu, and J. Fisher, "Information theoretic learning," in: S. Haykin, Unsupervised Adaptive Filtering, Wiley, New York, pp. 265-319, 2000.
  9. E. Parzen, "On the estimation of a probability density function and the mode," J. Ann. Math. Stat. vol. 33, issue 3, pp. 1065-1076, 1962. https://doi.org/10.1214/aoms/1177704472
  10. K. Jeong, J. Xu, D. Erdogmus, and J. Principe, "A new classifier based on information theoretic learning with unlabeled data," Neural Networks, vol. 18, no. 5-6, pp. 719-726, 2005. https://doi.org/10.1016/j.neunet.2005.06.018
  11. N. Kim, "Adaptive equalization using PDF matching algorithms for underwater communication channels with impulsive noise," J. KICS, vol. 36, no. 10, pp. 1210-1215, Oct. 2011. https://doi.org/10.7840/KICS.2011.36B.10.1210
  12. P. Delaney, "Signal detection in multivariate class A interference," IEEE Trans. Commun., vol. 43, no. 2/3/4, pp. 365-373, Feb/Mar./Apr. 1995. https://doi.org/10.1109/26.380055