On-line blind equalization via on-line blind separation

Abstract A blind equalization method based on the theory of independent component analysis is presented. The blind equalization of an unknown FIR channel with possibly non-minimum phase is formulated as a blind separation problem. The inputs to the equalizer are formed by stacking the fractional samples of the channel outputs. New on-line blind equalization algorithms are developed from on-line blind separation algorithms, thus they inherit the equivariant property from the equivariant blind separation algorithms. Due to this property, the performance of the new algorithms is independent of the channel parameters. Therefore, they are useful for equalizing some ill-conditioned channels. It is shown by simulations that the proposed algorithm works well when the input sequence has some degree of correlation.

[1]  Shun-ichi Amari,et al.  Adaptive Online Learning Algorithms for Blind Separation: Maximum Entropy and Minimum Mutual Information , 1997, Neural Computation.

[2]  E. Chng.  An On-line Learning Algorithm for Blind Equalization , 1996 .

[3]  S. Amari,et al.  Blind equalization of switching channels by ICA and learning of learning rate , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[5]  Philippe Loubaton,et al.  Subspace method for blind separation of sources in convolutive mixture , 1996, 1996 8th European Signal Processing Conference (EUSIPCO 1996).

[6]  T. Kailath,et al.  A least-squares approach to blind channel identification , 1995, IEEE Trans. Signal Process..

[7]  S.C. Douglas,et al.  Multichannel blind deconvolution and equalization using the natural gradient , 1997, First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications.

[8]  Y. Hua,et al.  Source correlation compensation for blind channel identification based on second-order statistics , 1994, IEEE Signal Processing Letters.

[9]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[10]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[11]  Sasan H. Ardalan,et al.  A generalized decision-directed blind equalization algorithm applied to equalization of multipath rayleigh fading mobile communication channels , 1992 .

[12]  Shun-ichi Amari,et al.  Novel On-Line Adaptive Learning Algorithms for Blind Deconvolution Using the Natural Gradient Approach , 1997 .

[13]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[14]  Shun-ichi Amari,et al.  Recurrent Neural Networks For Blind Separation of Sources , 1995 .

[15]  Lang Tong,et al.  Blind identification and equalization based on second-order statistics: a time domain approach , 1994, IEEE Trans. Inf. Theory.

[16]  Sheng Chen,et al.  Multi-stage blind clustering equaliser , 1995, IEEE Trans. Commun..

[17]  J. Cadzow.  Blind deconvolution via cumulant extrema , 1996, IEEE Signal Process. Mag..

[18]  Pierre Comon.  Independent component analysis - a new concept? signal processing , 1994 .

[19]  Philippe Loubaton,et al.  Second order blind equalization in multiple input multiple output FIR systems: a weighted least squares approach , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[20]  Shun-ichi Amari,et al.  Two Gradient Descent Algorithms for Blind Signal Separation , 1996, ICANN.

[21]  S. H. Ardalan,et al.  A soft decision-directed blind equalization algorithm applied to equalization of mobile communication channels , 1992, [Conference Record] SUPERCOMM/ICC '92 Discovering a New World of Communications.

[22]  Yingbo Hua,et al.  Previously Published Works Uc Riverside Title: Fast Maximum Likelihood for Blind Identification of Multiple Fir Channels Fast Maximum Likelihood for Blind Identification of Multiple Fir Channels , 2022 .

[23]  Eric Moulines,et al.  Subspace methods for the blind identification of multichannel FIR filters , 1995, IEEE Trans. Signal Process..