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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.08326 |
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| _version_ | 1866911125836136448 |
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| author | Straßhofer, Andreas Lentner, Diego Liva, Gianluigi Amat, Alexandre Graell i |
| author_facet | Straßhofer, Andreas Lentner, Diego Liva, Gianluigi Amat, Alexandre Graell i |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_08326 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Soft-Information Post-Processing for Chase-Pyndiah Decoding Based on Generalized Mutual Information Straßhofer, Andreas Lentner, Diego Liva, Gianluigi Amat, Alexandre Graell i Information Theory 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. |
| title | Soft-Information Post-Processing for Chase-Pyndiah Decoding Based on Generalized Mutual Information |
| topic | Information Theory |
| url | https://arxiv.org/abs/2308.08326 |