Fourth-Order cumulants and neural network approach for robust blind channel equalization

  • Authors:

  • Soowhan Han;Kwangeui Lee;Jongkeuk Lee;Fredric M. Ham

  • Affiliations:

  • Department of Multimedia Engineering Dongeui University, Busan, Korea;Department of Multimedia Engineering Dongeui University, Busan, Korea;Department of Computer Engineering, Dongeui University, Busan, Korea;Department of Electrical and Computer Engineering, Florida Institute of Technology, Melbourne, Florida

  • Venue:
  • ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
  • Year:
  • 2005

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Abstract

This study addresses a new blind channel equalization method using fourth-order cumulants of channel inputs and a three-layer neural network equalizer. The proposed algorithm is robust with respect to the existence of heavy Gaussian noise in a channel and does not require the minimum-phase characteristic of the channel. The transmitted signals at the receiver are over-sampled to ensure the channel described by a full-column rank matrix. It changes a single-input/single-output (SISO) finite-impulse response (FIR) channel to a single-input/multi-output (SIMO) channel. Based on the properties of the fourth-order cumulants of the over-sampled channel inputs, the iterative algorithm is derived to estimate the deconvolution matrix which makes the overall transfer matrix transparent, i.e., it can be reduced to the identity matrix by simple reordering and scaling. By using this estimated deconvolution matrix, which is the inverse of the over-sampled unknown channel, a three-layer neural network equalizer is implemented at the receiver. In simulation studies, the stochastic version of the proposed algorithm is tested with three-ray multi-path channels for on-line operation, and its performance is compared with a method based on conventional second-order statistics. Relatively good results, with fast convergence speed, are achieved, even when the transmitted symbols are significantly corrupted with Gaussian noise.