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

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Information Potential and Blind Algorithms Using a Biased Distribution of Random-Order Symbols

랜덤 심볼열의 바이어스된 분포를 이용한 정보 포텐셜과 블라인드 알고리즘

  • 김남용 (강원대학교 전자정보통신공학부)
  • Received : 2012.10.16
  • Accepted : 2012.12.24
  • Published : 2013.01.31
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Abstract

Blind algorithms based on Information potential of output samples and a set of symbols generated in random order at the receiver go through performance degradation when biased impulsive noise is added to the channel since the cost function composed of information potentials has no variable to deal with biased signal. Aiming at the robustness against biased impulsive noise, we propose, in this paper, a modified information potential, and derived related blind algorithms based on augmented filter structures and a set of random-order symbols. From the simulation results of blind equalization for multipath channels, the blind algorithm based on the proposed information potential produced superior convergence performance in the environments of strong biased impulsive noise.

출력 샘플과 수신단에서 랜덤한 순서로 발생된 심볼의 정보 포텐셜을 기반으로 한 블라인드 알고리즘은, 바이어스된 충격성 잡음이 채널에 더해질 때, 정보 포텐셜을 바탕으로 한 비용함수에 바이어스된 신호를 처리할 변수가 포함되어 있지 않아 성능저하를 겪게 된다. 이러한 바이어스된 충격성 잡음에 대한 강건성을 목표로, 이 논문에서는 수정된 정보 포텐셜을 제안하고, 이 제안된 정보 포텐셜에 기반하여 증강된 필터 구조와 랜덤 심볼을 사용한 새로운 블라인드 알고리즘을 도출하였다. 다중 경로 채널의 블라인드 등화에 대한 시뮬레이션 결과로부터, 제안된 정보 포텐셜에 기반한 블라인드 알고리즘이 바이어스된 강한 충격성 잡음 환경에서 탁월한 수렴 성능을 나타냈다.

Keywords

References

  1. J. Proakis, Digital Communications, McGraw-Hill, 2nd ed, 1989.
  2. Z. Daifeng, and Q. Tianshuang, "Underwater sources location in non-Gaussian impulsive noise environments", Digit. Signal Process., vol. 16, no. 2, pp. 149-163, Mar. 2006. https://doi.org/10.1016/j.dsp.2005.04.008
  3. K. Blackard, T. Rappaport, and C. Bostian, "Measurements and models of radio frequency impulsive noise for indoor wireless communications", IEEE J. Sel. Area Comm., vol. 11, no. 7, pp. 991-1001, Sep. 1993. https://doi.org/10.1109/49.233212
  4. S. Kim, S. Ahn, Y. Lee, S. Yoo and S. Youn, "DS/SS code acquisition scheme based on signed-rank statistic in non-Gaussian impulsive noise environments", J. KICS, vol. 33, no. 2, pp. 200-207, Feb. 2008.
  5. J. Armstrong, J. Shentu, C. Chai, and H. Suraweera, "Analysis of impulse noise mitigation techniques for digital television systems", in Proc. IEEE Int. OFDM Workshop (InOWo), pp. 172-176, Sept. 2003.
  6. N. Kim, and H. Byun, "Blind equalization based on Euclidian distance of information theoretic learning for impulsive noise environments", in Proc. IEEE Int. conf. on comp. commun. netw., pp. 53-56, Jul. 2010.
  7. A. Reny, "On measures of entropy and information", In Proc. Fourth Berkeley Symp. Math. Stat. Prob., 1960, vol. 1, Berkeley, University of California, p. 547, 1961.
  8. J. Principe, D. Xu and J. Fisher, "Information theoretic learning", in: S. Haykin, Unsupervised Adaptive Filtering, Wiley, New York, vol. I, pp. 265-319, March, 2000.
  9. I. Santamaria, P. P. Pokharel, and J. C. Principe, "Generalized correlation function: definition, properties, and application to blind equalization", IEEE Trans. Sig. Proces., vol. 54, no. 6, pp. 2187-2197, Jun. 2006. https://doi.org/10.1109/TSP.2006.872524