Complex Valued Recurrent Neural Networks for Blind Equalization

R. Pandey

References

  1. [1] S.I. Amari & A. Cichocki, Adaptive blind signal processing:Neural network approaches, Proc. IEEE, 86(10), 1998, 2026–2048. doi:10.1109/5.720251
  2. [2] S. Chen, G.J. Gibson, C.F.N. Cowan, & P.M. Grant, Adaptive equalization of finite nonlinear channels using multilayerperceptrons, Signal Processing, 20(2), 1990, 107–109. doi:10.1016/0165-1684(90)90122-F
  3. [3] T.W.S. Chow & Y. Fang, Neural blind deconvolution of MIMOnoisy channels, IEEE Trans. Circuits and Systems I, 48(1),2001, 116–120. doi:10.1109/81.903195
  4. [4] H. Lin & M. Amin, A dual mode technique for improved blindequalization for QAM signals, IEEE Signal Processing Letters,10(2), 2003, 29–31. doi:10.1109/LSP.2002.806699
  5. [5] M.N. Thirion & E. Moreau, Generalized criterion for blind mul-tivariate signal equalization, IEEE Signal Processing Letters,9(2), 2002, 72–74. doi:10.1109/97.991142
  6. [6] Y. Fang & T.W.S. Chow, Blind equalization of a noisy channelby linear neural network, IEEE Trans. Neural Networks, 10(4),1999, 925–929. doi:10.1109/72.774261
  7. [7] H.H. Yang, On-line blind equalization via on-line blind separation, Signal Processing, 68(2), 1998, 271–281. doi:10.1016/S0165-1684(98)00077-2
  8. [8] S. Haykin (Ed.), Blind deconvolution (Englewood Cliffs, NJ:Prentice-Hall, 1994).
  9. [9] S. Haykin, Adaptive filter theory, 3rd ed. (Upper Saddle River,NJ: Prentice-Hall, 1996).
  10. [10] C. You & D. Hong, Nonlinear blind equalization schemes usingcomplex valued multilayer feedforward neural networks, IEEETrans. Neural Networks, 9(6), 1998, 1442–1455.186 doi:10.1109/72.728394
  11. [11] A. Cichocki & R. Unbehauen, Neural networks for optimizationand signal processing (Chichester: John Wiley & Sons, 1994).
  12. [12] S. Haykin, Neural networks: A comprehensive foundation(Upper Saddle River, NJ: Prentice-Hall, 1994).
  13. [13] F.L. Luo & R. Unbehauen, Applied neural networks for signalprocessing (Cambridge: Cambridge University Press, 1997).
  14. [14] S. Haykin, Neural networks expand SP’s horizons, IEEE SignalProcessing Magazine, 3, 1996, 24–49. doi:10.1109/79.487040
  15. [15] G. Kechriotis, E. Zervas, & E.S. Manolakos, Using recurrentneural networks for adaptive communication channel equaliza-tion, IEEE Trans. Neural Networks, 5(2), 1994, 267–278. doi:10.1109/72.279190
  16. [16] R. Parisi, D. Di Elio Claudio, G. Orlandi, & B.D. Rao,Fast adaptive digital equalization by recurrent neural network,IEEE Trans. Signal Processing, 45(11), 1997, 2731–2739. doi:10.1109/78.650099
  17. [17] J.G. Proakis, Digital communications, 3rd ed. (Singapore:McGraw Hill, 1995).
  18. [18] M. Ghosh, Blind decision feedback equalization for terrestrialtelevision receivers, Proc. IEEE, 86(10), 1998, 2070–2081. doi:10.1109/5.720253
  19. [19] S. Ong, C. You, S. Choi, & D. Hong, A decision feedbackrecurrent neural equalizer as an infinite impulse response filter,IEEE Trans. Signal Processing, 45(11), 1997, 2851–2858. doi:10.1109/78.650112
  20. [20] C.R. Johnson, P. Schniter, T.J. Endres, J.D. Behm, D.R.Brown, & R.A. Casas, Blind equalization using the constantmodulus criterion: A review, Proc. IEEE, 86(10), 1998, 1927–1950. doi:10.1109/5.720246
  21. [21] G. Picchi & G. Prati, Blind equalization and carrier recoveryusing a “stop-and-go decision directed algorithm, IEEE Trans.Communications, 35(9), 1987, 877–887. doi:10.1109/TCOM.1987.1096877

Important Links:

Go Back