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

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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An Adaptive Radial Basis Function Network algorithm for nonlinear channel equalization

  • Kim Nam yong (Samcheok National University, Deprt. of Comm. & Inform.)
  • Published : 2005.03.01
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Abstract

The authors investigate the convergence speed problem of nonlinear adaptive equalization. Convergence constraints and time constant of radial basis function network using stochastic gradient (RBF-SG) algorithm is analyzed and a method of making time constant independent of hidden-node output power by using sample-by-sample node output power estimation is derived. The method for estimating the node power is to use a single-pole low-pass filter. It is shown by simulation that the proposed algorithm gives faster convergence and lower minimum MSE than the RBF-SG algorithm.

Keywords

References

  1. S. Chen, G. J. Gibson, C. F. N. Cowan and P. M. Grant, 'Adaptive equalization of finite nonlinear channels using multiplayer perceptrons,' Signal Processing, vol. 20, pp. 107-119, 1990 https://doi.org/10.1016/0165-1684(90)90122-F
  2. M. Peng, C. L. Nikias and J. G. Proakis, 'Adaptive equalization with neural networks; new multiplayer perceptron structures and their evaluation,' in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, vol. 2. pp. 301-304, 1992
  3. N. W. K. Lo and H. M. Hafez, 'Neural Network Channel Equalization,' in Proc. IEEE Int. Conf. Neural Networks, vol. 2. pp. 981-986, 1992
  4. T. Kohonen, K. Ravio, O. Simula, O. Venta and L. Henriksson, 'Combining linear equalization and self organizing adaptation in dynamic discrete-signal detection,' in Proc. IEEE Int. Joint Conf. Neural Networks, vol. 1. pp. 223-228, 1990
  5. S. Chen, G. J. Gibson and P. M. Grant, 'Reconstruction of binary signals using an adaptive radial basis function equalizer,' Signal Processing, vol. 22, pp. 77-93, 1991 https://doi.org/10.1016/0165-1684(91)90030-M
  6. S. Chen, C. F. N. Cowan and P. M. Grant, 'Orthogonal least squares learning algorithm for radial basis function networks,' IEEE Trans. Neural Networks, vol. 2, pp. 302-309, Mar. 1991 https://doi.org/10.1109/72.80341
  7. S. Chen, B. Mulgrew and S. Mclaughlin, 'Adaptive Bayesian decision feedback equalizer based on a radial basis function networks,' in Proc. IEEE Int. Conf. Commun., pp. 1267-1271, 1992
  8. S. Chen, B. Mulgrew and P. M. Grant, 'A clustering technique for digital communications channel equalization using radial basis function networks,'IEEE Trans. Neural Networks, vol. 4. pp. 570-579, 1993 https://doi.org/10.1109/72.238312
  9. B. J. Oh, K. C. Kwak, S. S. Kim and J. W. Ryu, 'A method of designing nonlinear channel equalzier using conditional fuzzy C-means clustering' in Proc. IEEE Int. Conf. Signal Processing, vol. 2. pp. 1355-1358, 2002
  10. I. Cha and S. A. Kassam, 'Time-series prediction by adaptive radial basis function networks,' in Proc. IEEE Twenty-Sixth Annu. Conf. Inform. Sci., Syst., pp. 818-823, Mar. 1993
  11. I. Cha and S. A. Kassam, 'Channel equalization using adaptive complex radial basis function networks,' IEEE Journal on Selected Areas in Communications, vol. 13., pp. 122-131, Jan. 1995 https://doi.org/10.1109/49.363139
  12. B. Widrow and S. D. Stearns, Adaptive signal processing, Prentice-Hall, 1985
  13. D. J. Sebald and J. A. Bucklew, 'Support vector machine teclmiques for nonlinear equalization,' IEEE Trans. Signal Processing, vol. 48. pp. 3217-3226, Nov. 2000 https://doi.org/10.1109/78.875477