PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Nonlinear Channel Equalization with Gaussian Processes for Regression
Fernando Perez-Cruz, J. J. Murillo-Fuentes and Sebastian Caro
IEEE Transactions on Signal Processing Volume 56, Number 10-2, pp. 5283-5286, 2008.


We propose Gaussian processes for regression as a novel nonlinear equalizer for digital communications receivers. GPR’s main advantage, compared to previous nonlinear estimation approaches, lies on their capability to optimize the kernel hyperparameters by maximum likelihood, which improves its performance significantly for short training sequences. Besides, GPR can be understood as a nonlinear minimum mean square error estimator, a standard criterion for training equalizers that trades-off the inversion of the channel and the amplification of the noise. In the experiment section, we show that the GPR-based equalizer clearly outperforms support vector machine and kernel adaline approaches, exhibiting outstanding results for short training sequences.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4899
Deposited By:Fernando Perez-Cruz
Deposited On:24 March 2009