Blind equalization of a noisy channel by linear neural network

In this paper, a new neural approach is introduced for the problem of blind equalization in digital communications. Necessary and sufficient conditions for blind equalization are proposed, which can be implemented by a two-layer linear neural network. In the hidden layer, the received signals are whitened, while the network outputs provide directly an estimation of the source symbols. We consider a stochastic approximate learning algorithm for each layer according to the property of the correlation matrices of the transmitted symbols. The proposed class of networks yield good results in simulation examples for the blind equalization of a three-ray multipath channel.

[1]  Tatsuya Nomura,et al.  An extension of the Herault-Jutten network to signals including delays for blind separation , 1996, Neural Networks for Signal Processing VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop.

[2]  S. Amari,et al.  Multichannel blind separation and deconvolution of sources with arbitrary distributions , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[3]  Christoph E. Schreiner,et al.  Blind source separation and deconvolution: the dynamic component analysis algorithm , 1998 .

[4]  Juha Karhunen,et al.  Neural approaches to independent component analysis and source separation , 1996, ESANN.

[5]  R. Lambert.  Multichannel blind deconvolution: FIR matrix algebra and separation of multipath mixtures , 1996 .

[6]  Juha Karhunen,et al.  A Unified Neural Bigradient Algorithm for robust PCA and MCA , 1996, Int. J. Neural Syst..

[7]  Te-Won Lee,et al.  Blind Separation of Delayed and Convolved Sources , 1996, NIPS.

[8]  Erkki Oja,et al.  A class of neural networks for independent component analysis , 1997, IEEE Trans. Neural Networks.

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

[10]  Kurt Hornik,et al.  Learning in linear neural networks: a survey , 1995, IEEE Trans. Neural Networks.

[11]  A. Cichocki,et al.  Blind signal deconvolution by spatio-temporal decorrelation and demixing , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[12]  Hui Liu,et al.  Closed-form blind symbol estimation in digital communications , 1995, IEEE Trans. Signal Process..

[13]  Björn E. Ottersten,et al.  Statistical analysis of a subspace method for blind channel identification , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[14]  John G. Proakis,et al.  Digital Communications , 1983 .

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

[16]  Jennie Si,et al.  Blind equalization with a linear feedforward neural network , 1997, ESANN.

[17]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[18]  Kari Torkkola,et al.  Blind separation of convolved sources based on information maximization , 1996, Neural Networks for Signal Processing VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop.

[19]  A. Lee Swindlehurst,et al.  Algorithms for blind equalization with multiple antennas based on frequency domain subspaces , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[20]  E. Oja,et al.  On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix , 1985 .

[21]  J.K. Tugnait.  On blind MIMO channel estimation and blind signal separation in unknown additive noise , 1997, First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications.

[22]  Elias S. Manolakos,et al.  Using recurrent neural networks for adaptive communication channel equalization , 1994, IEEE Trans. Neural Networks.

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