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.

翻译：Chase-Pyndiah译码被广泛用于乘积码的译码。然而，该方法并非最优，需要在迭代处理过程中对交换的软信息进行缩放。本文提出了一种基于最大化广义互信息来获取缩放系数的框架。与使用启发式系数的原始Chase-Pyndiah译码器相比，本方法在二进制输入加性高斯白噪声信道上，对于采用双纠错扩展BCH分量码的乘积码，可获得高达0.11 dB的增益。我们还引入了Chase-Pyndiah译码器的外信息版本，并将乘积码与类Turbo码编码组合相关联，以推导出基于蒙特卡罗的密度进化分析。由此得到的迭代译码阈值能够准确预测瀑布区的起始点。