Improved Chase-Pyndiah Decoding for Product Codes with Scaled Messages

2026-04-22

AI Summary

This work addresses the suboptimal utilization of extrinsic information in Chase-Pyndiah decoding by proposing a dynamic extrinsic information scaling mechanism based on component decoder confidence. During iterative decoding, the method adaptively adjusts the weight of extrinsic information according to local reliability metrics, thereby enabling more effective propagation of trustworthy soft information. The proposed scheme introduces negligible computational overhead while achieving approximately 0.1 dB gain in error-rate performance over the original Chase-Pyndiah decoder on standard product codes, significantly enhancing both decoding efficiency and reliability.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Information Theory

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

We propose an enhanced Chase-Pyndiah decoder that scales extrinsic messages based on decoder confidence of the component decoder, achieving a 0.1 dB gain over the original with negligible complexity increase.

Problem

• Chase-Pyndiah decoding
• product codes
• extrinsic message scaling
• decoder confidence
• error correction

Innovation

• Chase-Pyndiah decoding
• product codes
• scaled extrinsic messages
• decoder confidence
• iterative decoding