PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Soft LDPC decoding in nonlinear channels with Gaussian processes for classification
Pablo Olmos, Juan Jose Murillo-Fuentes and Fernando Perez-Cruz
In: Eusipco, August, Glasgow UK.


In this paper, we propose a new approach for nonlinear equalization based on Gaussian processes for classification (GPC).We also measure the performance of the equalizer after a low-density parity-check channel decoder has detected the received sequence. Typically, most channel equalizers concentrate on reducing the bit error rate, instead of providing accurate posterior probability estimates. GPC is a Bayesian nonlinear classification tool that provides accurate posterior probability estimates with short training sequences. We show that the accuracy of these estimates is essential for optimal performance of the channel decoder and that the error rate outputted by the equalizer might be irrelevant to understand the performance of the overall communication receiver. We compare the proposed equalizers with state-ofthe- art solutions.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6755
Deposited By:Fernando Perez-Cruz
Deposited On:08 March 2010