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Optimum Solutions of Minimum Error Entropy Algorithm

최소 오차 엔트로피 알고리듬의 최적해

  • Kim, Namyong (Division of Electronics, Information & Communication Engineering, Kangwon National Unversity) ;
  • Lee, Gyoo-yeong (Division of Electronics, Information & Communication Engineering, Kangwon National Unversity)
  • Received : 2015.11.03
  • Accepted : 2016.04.21
  • Published : 2016.06.30

Abstract

The minimum error entropy (MEE) algorithm is known to be superior in impulsive noise environment. In this paper, the optimum solutions and properties of the MEE algorithm are studied in regard to the robustness against impulsive noise. From the analysis of the behavior of optimum weight and factors related with mitigation of influence from large errors, it is revealed that the magnitude controlled input entropy plays the main role of keeping optimum weight of MEE undisturbed from impulsive noise. In the simulation, the optimum weight of MEE is shown to be the same as that of MSE criterion.

최소오차 엔트로피 알고리듬(MEE)은 충격성 잡음 환경에서 성능이 우수한 것으로 알려져 있다. 이 논문에서는 충격성 잡음에 대한 강인성의 견지에서 MEE 알고리듬의 최적해와 특성을 연구하였다. 큰 오차 값의 영향을 경감하는 요인들과 최적 가중치의 움직임에 대한 분석을 통하여 MEE의 최적해가 충격성 잡음으로부터 안정적으로 유지되도록 하는 주된 역할은 크기 조정된 입력 엔트로피가 담당하고 있음이 밝혀졌다. 시뮬레이션 결과에서, MEE의 최적해는 MSE 성능기준의 최적해와 같은 값을 가짐을 보였다.

Keywords

References

  1. N. Kim, H. Byun, Y. You and K. Kwon, "Blind signal processing for impulsive noise channels", JCN, vol. 14, pp. 27-33, Feb. 2012. DOI: http://dx.doi.org/10.1109/JCN.2012.6184548
  2. J. Armstrong, J. Shentu, C. Chai, and H. Suraweera, "Analysis of impulse noise mitigation techniques for digital television systems", Proceedings of InOWo '03, pp. 172-176. 2003. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.399.3297&rep=rep1&type=pdf
  3. I. Santamaria, P. Pokharel, and J. Principe, "Generalized correlation function: Definition, properties, and application to blind equalization", IEEE Trans. Signal Processing, vol.54, pp. 2187-2197, June 2006. http://dx.doi.org/10.1109/TSP.2006.872524
  4. D. Erdogmus, and J. Principe, "An error-entropy minimization algorithm for supervised training of nonlinear systems", IEEE Trans. Signal Processing, vol. 50, pp. 1780-1786, July, 2002. http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1011217 https://doi.org/10.1109/TSP.2002.1011217
  5. N. Kim, "Decision Feedback Equalizer Algorithms based on Error Entropy Criterion", Journal of Internet Computing and Services, vol. 12, pp. 27-33. Aug. 2011. http://www.jksii.or.kr/upload/1/860_1.pdf
  6. N. Kim, "Performance analysis of entropy-based decision feedback algorithms in wireless shallow-water communications", Proceedings of KSII summer conference, pp. 185-186, June, 2012. http://www.dbpia.co.kr/Journal/ArticleDetail/NODE01949574
  7. N. Kim and A. Andonova, "Computationally efficient methods for decision feedback algorithms based on minimum error entropy", Annual Journal of Electronics, ISSN 1314-0078, pp 17-19. 2014. http://ecad.tu-sofia.bg/et/2014/ET2014/AJE_2014/017-A_Andonova.pdf
  8. S. Haykin, Adaptive Filter Theory, Prentice Hall, Upper Saddle River, 4th ed, 2001. http://tocs.ulb.tu-armstadt.de/110863747.pdf
  9. E. Parzen, "On the estimation of a probability density function and the mode," Ann. Math. Stat. vol. 33, p.1065, 1962. http://bayes.wustl.edu/Manual/parzen62.pdf
  10. J. Proakis, Digital Communications, McGraw-Hill, 2nd ed, 1989. http://www.slideshare.net/hoangphuong2808/digital-communications-by-john-proakis-4th-edition