Iterative Neural Rollback Chase-Pyndiah Decoding

TL;DR

This paper introduces a neural network-assisted rollback Chase-Pyndiah decoding method that improves error correction performance in turbo product codes by preventing destructive extrinsic information updates, leading to better bit error rates.

Contribution

It proposes a transformer-based neural network to identify and prevent harmful updates in Chase-Pyndiah decoding, enhancing performance over traditional methods.

Findings

Achieves approximately 0.145 dB gain in bit error rate performance.

Effectively distinguishes destructive updates with a small neural network.

Outperforms conventional Chase p=7 decoding in TPC schemes.

Abstract

Iterative decoding is essential in modern communication systems, especially optical communications, where error-correcting codes such as turbo product codes (TPC) and staircase codes are widely employed. A key factor in achieving high error correction performance is the use of soft-decision decoding for component codes. However, implementing optimal maximum a posteriori (MAP) probability decoding for commonly used component codes, such as BCH and Polar codes, is computationally prohibitive. Instead, practical systems rely on approximations, with the Chase-Pyndiah algorithm being a widely used suboptimal method. TPC are more powerful than their component codes and begin to function effectively at low signal-to-noise ratios. Consequently, during the initial iterations, the component codes do not perform well and introduce errors in the extrinsic information updates. This phenomenon limits the performance of TPC. This paper proposes a neural network-aided rollback Chase-Pyndiah decoding method to address this issue. A transformer-based neural network identifies cases where extrinsic updates are likely to introduce errors, triggering a rollback mechanism which prevents the update and keeps the component code message intact. Our results demonstrate that a neural network with a relatively small number of parameters can effectively distinguish destructive updates and improve decoding performance. We evaluate the proposed approach using TPC with (256, 239) extended BCH component codes. We show that the proposed method enhances the bit error rate performance of Chase-Pyndiah p=6 decoding, achieving a gain of approximately 0.145 dB in a TPC scheme with four full iterations, significantly outperforming conventional Chase p=7 decoding.