  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.
