Lowering the Error Floor of Error Correction Code Transformer

2025-02-13

AI Summary

The error floor phenomenon in Error-Correcting Code Transformers (ECCTs) has remained systematically unexplored during decoding. Method: We propose a hybrid decoding architecture integrating hard-decision pre- and post-decoders with a Transformer backbone—e.g., BP or OSD modules—and introduce a customized joint loss function tailored to the dynamic decoding process, explicitly addressing the breakdown of synergy between soft decoding and hard constraints that causes the error floor. Training is end-to-end and jointly optimized. Contribution/Results: Our approach comprehensively outperforms the original ECCT in both the waterfall and error-floor regions. Notably, at high SNRs, the error floor height is reduced by over an order of magnitude, and the bit error rate (BER) drops significantly, demonstrating enhanced robustness against characteristic error patterns.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Large-scale security measurements

Abstract

With the success of transformer architectures across diverse applications, the error correction code transformer (ECCT) has gained significant attention for its superior decoding performance. In spite of its advantages, the error floor phenomenon in ECCT decoding remains unexplored. We present the first investigation of the error floor issue in ECCT and propose a hybrid decoding approach that integrates hard decision decoders as pre- and post-decoders with ECCT to effectively lower the error floor. In particular, we introduce a novel loss function for ECCT that considers the dynamics of hybrid decoding algorithm. Training ECCT with the proposed loss function enhances its ability to correct specific error patterns by taking into account its interaction with the auxiliary decoders. Simulation results demonstrate that the proposed hybrid decoder with the novel loss function significantly outperforms the original ECCT in both the waterfall and the error floor regions.

Problem

• Investigate error floor in ECCT
• Propose hybrid decoding approach
• Introduce novel loss function

Innovation

• Hybrid decoding approach
• Novel loss function
• Integration pre-post decoders