Error Correction Code Transformer: From Non-Unified to Unified

2024-10-04

arXiv.org

AI Summary

To address the flexible multi-code-type decoding requirements of 6G wireless communications, conventional dedicated hardware decoders suffer from poor adaptability and limited scalability. This paper proposes the first code-agnostic unified Transformer-based decoder capable of decoding diverse linear block codes—including Polar, LDPC, and BCH codes. Our method introduces three key innovations: (1) standardized parameter units and a redesigned unified attention module; (2) a structured sparse mask derived from parity-check matrix sparsity to explicitly model inter-bit constraints; and (3) cross-code-type parameter normalization and structural encoding mechanisms. Experiments demonstrate that the proposed decoder consistently outperforms traditional belief propagation (BP) and state-of-the-art neural decoders across multiple code lengths and rates, achieving superior accuracy, robustness to channel variations, and hardware deployment efficiency. The architecture provides a scalable, unified decoding foundation for 6G systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Channel coding is vital for reliable data transmission in modern wireless systems, and its significance will increase with the emergence of sixth-generation (6G) networks, which will need to support various error correction codes. However, traditional decoders were typically designed as fixed hardware circuits tailored to specific decoding algorithms, leading to inefficiencies and limited flexibility. To address these challenges, this paper proposes a unified, code-agnostic Transformer-based decoding architecture capable of handling multiple linear block codes, including Polar, Low-Density Parity-Check (LDPC), and Bose-Chaudhuri-Hocquenghem (BCH), within a single framework. To achieve this, standardized units are employed to harmonize parameters across different code types, while the redesigned unified attention module compresses the structural information of various codewords. Additionally, a sparse mask, derived from the sparsity of the parity-check matrix, is introduced to enhance the model's ability to capture inherent constraints between information and parity-check bits, resulting in improved decoding accuracy and robustness. Extensive experimental results demonstrate that the proposed unified Transformer-based decoder not only outperforms existing methods but also provides a flexible, efficient, and high-performance solution for next-generation wireless communication systems.

Problem

• Proposes a unified Transformer-based decoding architecture
• Handles multiple linear block codes in one framework
• Enhances decoding accuracy and robustness for 6G networks

Innovation

• Unified Transformer-based decoding architecture
• Standardized units harmonize code parameters
• Sparse mask enhances decoding accuracy