纠错码Transformer中如何掩码：系统化掩码与双重掩码
How to Mask in Error Correction Code Transformer: Systematic and Double Masking
AI总结：
针对提升纠错码Transformer（ECCT）解码性能的问题，提出系统化掩码矩阵和双重掩码ECCT架构两种方法，通过优化掩码设计学习更多样化的码字比特关系特征，最终以显著优势取得当前最优解码性能。
摘要：
在通信与存储系统中，纠错码（ECCs）是保障数据可靠性的关键。随着深度学习的应用范围拓展至不同领域，性能优于传统解码算法的基于神经网络的解码器日益成为研究热点。在这类神经解码器中，纠错码Transformer（ECCT）取得了当前最优性能，以大幅优势领先其他方法。为进一步提升ECCT的性能，我们提出两种新方法。第一，利用纠错码的系统化编码技术，为ECCT设计了一种新的掩码矩阵，旨在提升性能并降低计算复杂度。第二，提出一种名为双重掩码ECCT的新型ECCT Transformer架构，该架构在掩码自注意力块中并行采用两种不同的掩码矩阵，以学习码字比特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，以显著优势取得了当前最优的解码性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
