How to Mask in Error Correction Code Transformer: Systematic and Double Masking

• URL: http://arxiv.org/abs/2308.08128v2
• Date: Fri, 25 Aug 2023 04:53:19 GMT
• Title: How to Mask in Error Correction Code Transformer: Systematic and Double Masking
• Authors: Seong-Joon Park, Hee-Youl Kwak, Sang-Hyo Kim, Sunghwan Kim, Yongjune Kim, Jong-Seon No
• Abstract summary: In communication and storage systems, error correction codes (ECCs) are pivotal in ensuring data reliability. Error Correction Code Transformer (ECCT) has achieved the state-of-the-art performance, outperforming other methods by large margins. We propose a new masking matrix for ECCT, aiming to improve the performance and reduce the computational complexity.
• Score: 16.90917067964835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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