Error correction code is a major part of the physical communication layer, ensuring the reliable transfer of data over noisy channels.Recently, neural decoders were shown to outperform classical decoding techniques.However, the existing neural approaches present strong overfitting, due to the exponential training complexity, or a restrictive inductive bias, due to reliance on Belief Propagation.Recently, Transformers have become methods of choice in many applications, thanks to their ability to represent complex interactions between elements.In this work, we propose to extend for the first time the Transformer architecture to the soft decoding of linear codes at arbitrary block lengths.We encode each channel's output dimension to a high dimension for a better representation of the bits' information to be processed separately.The element-wise processing allows the analysis of channel output reliability, while the algebraic code and the interaction between the bits are inserted into the model via an adapted masked self-attention module.The proposed approach demonstrates the power and flexibility of Transformers and outperforms existing state-of-the-art neural decoders by large margins, at a fraction of their time complexity.

BCH(7,4) code with N = 6, d = 32, similarly to the results presented in Section 6.1. Interestingly, in the early stage of the decoding, ECCT seems to focus its processing of the syndrome. Once the network corrects the bit (last layers(s)), the values return to normal. Using multiple heads is clearly beneficial for the model's performance. The proposed shallow ECCTs are able to compete and even surpass the SCL for some of the codes and SNRs.

We encode each channel's output dimension to a high dimension for better representation of the bits' information to be processed separately.

Error correction code is a major part of the physical communication layer, ensuring the reliable transfer of data over noisy channels.Recently, neural decoders were shown to outperform classical decoding techniques.However, the existing neural approaches present strong overfitting, due to the exponential training complexity, or a restrictive inductive bias, due to reliance on Belief Propagation.Recently, Transformers have become methods of choice in many applications, thanks to their ability to represent complex interactions between elements.In this work, we propose to extend for the first time the Transformer architecture to the soft decoding of linear codes at arbitrary block lengths.We encode each channel's output dimension to a high dimension for a better representation of the bits' information to be processed separately.The element-wise processing allows the analysis of channel output reliability, while the algebraic code and the interaction between the bits are inserted into the model via an adapted masked self-attention module.The proposed approach demonstrates the power and flexibility of Transformers and outperforms existing state-of-the-art neural decoders by large margins, at a fraction of their time complexity.

Channel coding is vital for reliable data transmission in modern wireless systems, and its significance will increase with the emergence of sixth-generation (6G) networks, which will need to support various error correction codes. However, traditional decoders were typically designed as fixed hardware circuits tailored to specific decoding algorithms, leading to inefficiencies and limited flexibility. To address these challenges, this paper proposes a unified, code-agnostic Transformer-based decoding architecture capable of handling multiple linear block codes, including Polar, Low-Density Parity-Check (LDPC), and Bose-Chaudhuri-Hocquenghem (BCH), within a single framework. To achieve this, standardized units are employed to harmonize parameters across different code types, while the redesigned unified attention module compresses the structural information of various codewords. Additionally, a sparse mask, derived from the sparsity of the parity-check matrix, is introduced to enhance the model's ability to capture inherent constraints between information and parity-check bits, resulting in improved decoding accuracy and robustness. Extensive experimental results demonstrate that the proposed unified Transformer-based decoder not only outperforms existing methods but also provides a flexible, efficient, and high-performance solution for next-generation wireless communication systems.

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.