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.

翻译：信道编码对于现代无线系统中的可靠数据传输至关重要，其重要性将随着第六代（6G）网络的出现而日益凸显，因为6G网络需要支持多种纠错码。然而，传统解码器通常被设计为针对特定解码算法定制的固定硬件电路，导致效率低下且灵活性有限。为应对这些挑战，本文提出了一种统一的、与码型无关的基于Transformer的解码架构，能够在单一框架内处理多种线性分组码，包括Polar码、低密度奇偶校验（LDPC）码和Bose-Chaudhuri-Hocquenghem（BCH）码。为实现这一目标，我们采用标准化单元来协调不同码型间的参数，同时重新设计的统一注意力模块压缩了各种码字的结构信息。此外，通过利用奇偶校验矩阵的稀疏性引入稀疏掩码，增强了模型捕捉信息位与校验位之间固有约束的能力，从而提高了解码精度和鲁棒性。大量实验结果表明，所提出的基于Transformer的统一解码器不仅性能优于现有方法，还为下一代无线通信系统提供了一种灵活、高效且高性能的解决方案。