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arXiv · 2308.08326

Soft-Information Post-Processing for Chase-Pyndiah Decoding Based on Generalized Mutual Information

Andreas Straßhofer·Diego Lentner·Gianluigi Liva·Alexandre Graell i Amat
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Abstract

Chase-Pyndiah decoding is widely used for decoding product codes. However, this method is suboptimal and requires scaling the soft information exchanged during the iterative processing. In this paper, we propose a framework for obtaining the scaling coefficients based on maximizing the generalized mutual information. Our approach yields gains up to 0.11 dB for product codes with two-error correcting extended BCH component codes over the binary-input additive white Gaussian noise channel compared to the original Chase-Pyndiah decoder with heuristically obtained coefficients. We also introduce an extrinsic version of the Chase-Pyndiah decoder and associate product codes with a turbo-like code ensemble to derive a Monte Carlo-based density evolution analysis. The resulting iterative decoding thresholds accurately predict the onset of the waterfall region.

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Andreas Straßhofer, Diego Lentner, Gianluigi Liva, Alexandre Graell i Amat. 2023-08-16. Soft-Information Post-Processing for Chase-Pyndiah Decoding Based on Generalized Mutual Information. https://doi.org/10.1109/istc57237.2023.10273464

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