Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Blind linear/nonlinear equalization for heavy noise-corrupted channels

  • Han, Soo- Whan (Department of Multimedia Eng., Dong-Eui University) ;
  • Park, Sung-Dae (Department of Digital Culture and Contents Eng., Dong-Eui University)
  • Published : 2009.09.30
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Abstract

In this paper, blind equalization using a modified Fuzzy C-Means algorithm with Gaussian Weights (MFCM_GW) is attempted to the heavy noise-corrupted channels. The proposed algorithm can deal with both of linear and nonlinear channels, because it searches for the optimal channel output states of a channel instead of estimating the channel parameters in a direct manner. In contrast to the common Euclidean distance in Fuzzy C-Means (FCM), the use of the Bayesian likelihood fitness function and the Gaussian weighted partition matrix is exploited in its search procedure. The selected channel states by MFCM_GW are always close to the optimal set of a channel even the additive white Gaussian noise (AWGN) is heavily corrupted in it. Simulation studies demonstrate that the performance of the proposed method is relatively superior to existing genetic algorithm (GA) and conventional FCM based methods in terms of accuracy and speed.

Keywords

References

  1. E. Biglieri, A. Gersho, R.D. Gitlin and T.L. Lim, "Adaptive cancellation of nonlinear intersymbol interference for voiceband data transmission", IEEE J. Selected Areas Commun., SAC-2, Vol.5, pp. 765-777, 1984
  2. G. B. Giannakis and E. Serpedin, "Linear multichannel blind equalizers of nonlinear FIR volterra channels," IEEE Transactions on Signal Processing, vol. 45, pp. 67-81, 1997 https://doi.org/10.1109/78.552206
  3. J.G. Proakis, Digital Communications, Fourth Edition, McGraw-Hill, C 2001
  4. E. Serpedin and G.B. Giannakis, "Blind channel identification and equalization with modulationinduced cyclostationarity", IEEE Trans. Signal Processing, Vol. 46, pp. 1930-1944, 1998 https://doi.org/10.1109/78.700965
  5. Y. Fang, W.S. Chow and K.T. Ng, "Linear neural network based blind equalization", Signal Processing, Vol. 76, pp. 37-42, 1999 https://doi.org/10.1016/S0165-1684(98)00245-X
  6. Yun Ye and Saman S. Abeysekera, "Efficient blind estimation and equalization of non-minimum phase communication channels via the use of a zero forcing equalizer", Signal Processing, Vol. 86, pp.1019-1034, 2006 https://doi.org/10.1016/j.sigpro.2005.07.021
  7. T. Stathaki and A. Scohyers, "A constrained optimization approach to the blind estimation of Volterra kernels", Proc. IEEE Int. Conf on ASSP, Vol. 3, pp. 2373-2376, 1997 https://doi.org/10.1109/ICASSP.1997.599530
  8. G.K. Kaleh and R. Vallet, "Joint parameter estimation and symbol detection for linear or nonlinear unknown channels", IEEE Trans. Commun., Vol. 42, pp. 2406-2413,1994 https://doi.org/10.1109/26.297849
  9. D. Erdogmus, D. Rende, lC. Principe and T.F. Wong, "Nonlinear channel equalization using multilayer perceptrons with information theoretic criterion", Proc. of IEEE workshop Neural Networks and Signal Processing, pp. 443-451, MA, U.S.A., 2001 https://doi.org/10.1109/NNSP.2001.943148
  10. I. Santamaria, C. Pantaleon, L. Vielva and l Ibanez, "Blind Equalization of Constant Modulus Signals Using Support Vector Machines", IEEE Trans. Signal Processing, Vol. 52, pp. 1773-1782, 2004 https://doi.org/10.1109/TSP.2004.827176
  11. R. Lopez- Valcarce and S. Dasgupta, "Blind equalization of nonlinear channels from secondorder statistics," IEEE Transactions on Signal Processing, vol. 49, pp. 3084-3097, 2001 https://doi.org/10.1109/78.969516
  12. G. Raz and B. Van Veen, "Blind equalization and identification of nonlinear and IIR systems-a least squares approach", IEEE Trans. Signal Processing, Vol. 48, pp. 192-200, 2000 https://doi.org/10.1109/78.815489
  13. H. Lin and K. Yamashita, "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
  14. S. Han, W. Pedrycz and C. Han, "Nonlinear Channel Blind Equalization U sing Hybrid Genetic Algorithm with Simulated Annealing", Mathematical and Computer Modeling, Vol. 41, pp. 697-709, 2005 https://doi.org/10.1016/j.mcm.2004.05.006
  15. S. Han, "A Modified FCM for Nonlinear Blind Channel Equalization using RBF Networks," International Journal of KlMlCS, Vol. 5, pp. 35-41,2007
  16. R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973
  17. S. Chen, B. Mulgrew and M. Grant, "A clustering technique for digital communication channel equalization using radial basis function network", IEEE Trans. Neural Networks, Vol. 4, pp. 570-579, 1993 https://doi.org/10.1109/72.238312
  18. H. Lin and K. Yamashita, "Blind equalization using parallel Bayesian decision feedback equalizer", Mathematics and Computers in Simulation, Vol. 56, pp. 247-257, 2001 https://doi.org/10.1016/S0378-4754(01)00276-2
  19. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981
  20. 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