用低复杂度Transformer统一解码多种纠错码，提升6G通信效率
Unified Error Correction Code Transformer with Low Complexity
- 设计低秩统一注意力模块，将计算复杂度从O(N²)降至O(N)
- 通过稀疏掩码利用校验矩阵特性，使解码准确率提升且计算量减少86%
- 支持多类线性分组码的统一解码，适合6G硬件部署
信道编码对可靠第六代（6G）数据传输至关重要，需为不同应用场景采用多种纠错码。传统解码器需为每种码配备专用硬件，导致硬件成本高。近期提出的基于人工智能的方法，如纠错码Transformer（ECCT）及其增强版基础纠错码Transformer（FECCT），利用Transformer实现多码解码以降低硬件开销。然而，其因Transformer自注意力机制带来的$/mathcal{O}(N^2)$过高计算复杂度限制了可扩展性，其中$N$为序列长度。为此，我们提出一种统一的基于Transformer的解码器，可在单一框架内处理多种线性分组码。具体地，引入标准化单元统一不同码的长度与码率；设计一种计算复杂度为$/mathcal{O}(N)$的低秩统一注意力模块，供Transformer各注意力头共享；同时引入基于校验矩阵稀疏性的稀疏掩码，增强对信息位与校验位间固有约束的捕捉能力，提升解码准确率，并进一步降低86%的计算复杂度。大量实验表明，所提方法优于现有方法，为下一代无线通信系统提供了高性能、低复杂度的解决方案。
原文摘要 · Abstract (English)
Channel coding is vital for reliable sixth-generation (6G) data transmission, employing diverse error correction codes for various application scenarios. Traditional decoders require dedicated hardware for each code, leading to high hardware costs. Recently, artificial intelligence (AI)-driven approaches, such as the error correction code Transformer (ECCT) and its enhanced version, the foundation error correction code Transformer (FECCT), have been proposed to reduce the hardware cost by leveraging the Transformer to decode multiple codes. However, their excessively high computational complexity of $\mathcal{O}(N^2)$ due to the self-attention mechanism in the Transformer limits scalability, where $N$ represents the sequence length. To reduce computational complexity, we propose a unified Transformer-based decoder that handles multiple linear block codes within a single framework. Specifically, a standardized unit is employed to align code length and code rate across different code types, while a redesigned low-rank unified attention module, with computational complexity of $\mathcal{O}(N)$, is shared across various heads in the Transformer. Additionally, a sparse mask, derived from the parity-check matrix's sparsity, is introduced to enhance the decoder's ability to capture inherent constraints between information and parity-check bits, improving decoding accuracy and further reducing computational complexity by $86\%$. Extensive experimental results demonstrate that the proposed unified Transformer-based decoder outperforms existing methods and provides a high-performance, low-complexity solution for next-generation wireless communication systems.
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