Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Blind Nonlinear Channel Equalization by Performance Improvement on MFCM

MFCM의 성능개선을 통한 블라인드 비선형 채널 등화

  • 박성대 (동의대학교 컴퓨터공학과) ;
  • 우영운 (동의대학교 멀티미디어공학과) ;
  • 한수환 (동의대학교 멀티미디어공학과)
  • Published : 2007.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, a Modified Fuzzy C-Means algorithm with Gaussian Weights(MFCM_GW) is presented for nonlinear blind channel equalization. The proposed algorithm searches the optimal channel output states of a nonlinear channel from the received symbols, based on the Bayesian likelihood fitness function and Gaussian weighted partition matrix instead of a conventional Euclidean distance measure. Next, the desired channel states of a nonlinear channel are constructed with the elements of estimated channel output states, and placed at the center of a Radial Basis Function(RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a simplex genetic algorithm(GA), a hybrid genetic algorithm(GA merged with simulated annealing(SA): GASA), and a previously developed version of MFCM. It is shown that a relatively high accuracy and fast search speed has been achieved.

본 논문에서는 비선형 블라인드 채널등화기의 구현을 위하여 가우시안 가중치(gaussian weights)를 이용한 개선된 퍼지 클러스터(Modified Fuzzy C-Means with Gaussian Weights: MFCM_GW) 알고리즘을 제안한다. 제안된 알고리즘은 기존 FCM 알고리즘의 유클리디언 거리(Euclidean distance) 값 대신 Bayesian Likelihood 목적 함수(fitness function)와 가우시안 가중치가 적용된 멤버십 매트릭스(partition matrix)를 이용하여, 비선형 채널의 출력으로 수신된 데이터들로부터 최적의 채널 출력 상태 값(optimal channel output states)들을 직접 추정한다. 이렇게 추정된 채널 출력 상태 값들로 비선형 채널의 이상적 채널 상태(desired channel states) 백터들을 구성하고, 이를 Radial Basis Function(RBF) 등화기의 중심(center)으로 활용함으로써 송신된 데이터 심볼을 찾아낸다. 실험에서는 무작위 이진 신호에 가우시안 잡음이 추가된 데이터를 사용하여 기존의 Simplex Genetic Algorithm(GA), 하이브리드 형태의 GASA(GA merged with simulated annealing(SA)), 그리고 과거에 발표되었던 MFCM 등과 그 성능을 비교 분석하였으며, 가우시안 가중치가 적용된 MFCM_GW를 이용한 채널등화기가 상대적으로 정확도와 속도 면에서 우수함을 보였다.

Keywords

References

  1. Biglieri, E., Gersho, A., Gitlin, R. D., Lim, T. L ' Adaptive cancellation of nonlinear intersymbol interference for voiceband data transmission.' IEEE J. Selected Areas Comm. SAC-2(5) pp.765-777, 1984
  2. Proakis, J. G., Digital Communications, Fourth Edition, McGraw-Hill, New York, 2001
  3. Fang, Y., Chow, W. S., Ng, K. T., 'Linear neural network based blind equalization,' Signal Processing Vol.76, pp. 37-42, 1999 https://doi.org/10.1016/S0165-1684(98)00245-X
  4. Stathaki, T., Scohyers, A., 'A constrained optimization approach to the blind estimation of Volterra kernels,' Proc. IEEE Int. Conf. on ASSP 3, pp. 2373-2376, 1997
  5. Kaleh, G. K.,Vallet, R., 'Joint parameter estimation and symbol detection for linear or nonlinear unknown channels,' IEEE Trans. on Comm. Vol.42, pp. 2406 -2413, 1994 https://doi.org/10.1109/26.297849
  6. D. Erdogmus, D. Rende, J.C. Principe and T.F. Wong, 'Nonlinear channel equalization using multilayer perceptrons with information theoretic criterion,' Proc. of IEEE Workshop Neural Networks and Signal Processing, MA, USA,pp. 443-451, 2001
  7. I. Santamaria, C. Pantaleon, L. Vielva and J. Ibanez, 'Blind Equalization of Constant Modulus Signals Using Support Vector Machines,' IEEE Trans. on Signal Processing, Vol.52, pp. 1773-1782, 2004 https://doi.org/10.1109/TSP.2004.827176
  8. Lin, H., Yamashita, K., 'Hybrid simplex genetic algorithm for blind equalization using RBF networks,' Mathematics and Computers in Simulation Vol.59, pp. 293-304, 2002 https://doi.org/10.1016/S0378-4754(01)00364-0
  9. Soowhan Han, Imgeun Lee, Changwook Han, 'A New Hybird Genetic Algorithm for Nonlinear Channel Blind Equalization.' International Journal of Fuzzy Logic and Intelligent Systems, pp. 259-265, 2004
  10. Soowhan Han, 'A Modified FCM for Nonlinear Blind Channel Equalizer Using RBF Networks,' International Journal of KIMICS, Vol.5, No.1, pp,35-41, April 2007
  11. Duda, R. O., Hart, P. E., Pattern Classification and Scene Analysis, NewYork, Wiley, 1973
  12. S. Chen, C.F.N. Cowan, and P. M. Grant, 'Orthogonal Least Square Learning for Radial Basis Function Networks,' IEEE Trans. on Neural Networks, Vol.2, No.2, pp. 302-309, 1991 https://doi.org/10.1109/72.80341
  13. S. K. Patra and B. Mulgrew, 'Fuzzy techniques for adaptive nonlinear equalization,' Signal Processing, Vol.80, pp. 985-1000, 2000 https://doi.org/10.1016/S0165-1684(00)00015-3