CrossMPT: Cross-attention Message-Passing Transformer for Error Correcting Codes

Error correcting codes (ECCs) are indispensable for reliable transmission in communication systems. The recent advancements in deep learning have catalyzed the exploration of ECC decoders based on neural networks. Among these, transformer-based neural decoders have achieved state-of-the-art decoding performance. In this paper, we propose a novel Cross-attention Message-Passing Transformer (CrossMPT). CrossMPT iteratively updates two types of input vectors (i.e., magnitude and syndrome vectors) using two masked cross-attention blocks. The mask matrices in these cross-attention blocks are determined by the code's parity-check matrix that delineates the relationship between magnitude and syndrome vectors. Our experimental results show that CrossMPT significantly outperforms existing neural network-based decoders, particularly in decoding low-density parity-check codes. Notably, CrossMPT also achieves a significant reduction in computational complexity, achieving over a 50% decrease in its attention layers compared to the original transformer-based decoder, while retaining the computational complexity of the remaining layers.