In communication and storage systems, error correction codes (ECCs) are pivotal in ensuring data reliability. As deep learning's applicability has broadened across diverse domains, there is a growing research focus on neural network-based decoders that outperform traditional decoding algorithms. Among these neural decoders, Error Correction Code Transformer (ECCT) has achieved the state-of-the-art performance, outperforming other methods by large margins. To further enhance the performance of ECCT, we propose two novel methods. First, leveraging the systematic encoding technique of ECCs, we introduce a new masking matrix for ECCT, aiming to improve the performance and reduce the computational complexity. Second, we propose a novel transformer architecture of ECCT called a double-masked ECCT. This architecture employs two different mask matrices in a parallel manner to learn more diverse features of the relationship between codeword bits in the masked self-attention blocks. Extensive simulation results show that the proposed double-masked ECCT outperforms the conventional ECCT, achieving the state-of-the-art decoding performance with significant margins.

翻译：在通信与存储系统中，纠错码（ECC）对于保障数据可靠性至关重要。随着深度学习在多个领域应用的拓展，研究界日益关注基于神经网络的译码器，其性能优于传统译码算法。在这些神经译码器中，纠错码Transformer（ECCT）凭借以大幅领先其他方法的优势，达到了当前最优性能。为进一步提升ECCT的性能，我们提出两种新方法。首先，利用ECC的系统编码技术，我们为ECCT引入一种新型掩码矩阵，旨在提升性能并降低计算复杂度。其次，我们提出一种名为双重掩码ECCT的新型ECCT Transformer架构。该架构以并行方式采用两种不同的掩码矩阵，从而在掩码自注意力模块中学习码字比特之间关系的更多样化特征。大量仿真结果表明，所提出的双重掩码ECCT优于传统ECCT，以显著幅度实现了当前最优的译码性能。