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: IEEE ISIT 2010, June 13-18, 2010, Austin, TX.

Abstract

Expectation Propagation is a generalization to Belief Propagation (BP) in two ways. First, it can be used with any exponential family distribution over the clicks in the graph. Second, it can impose additional constraints on the marginal distributions. We use this second property to impose pair-wise marginal distribution constraints in some check nodes of the LDPC Tanner graph. These additional constraints allow decoding the received codeword when the BP decoder gets stuck. In this paper, we first present the new decoding algorithm, whose complexity is identical to the BP decoder, and we then prove that it is able to decode codewords with a larger fraction of erasures, as the block size tends to infinity. The proposed algorithm can be also understood as a simplification of the Maxwell decoder, but without its computational complexity. We also illustrate that the new algorithm outperforms the BP decoder for finite block-size codes.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7550
Deposited By:Fernando Perez-Cruz
Deposited On:17 March 2011