用Mamba替代注意力，实现高效长码纠错解码
Scalable Mamba-Based Message-Passing Neural Decoder for Error-Correcting Codes
- 基于图结构的局部聚合+双向Mamba块，避免密集注意力
- 在(1056,880)码上比顶尖方法提升0.45 dB，内存降低1.5倍
- 适合需要高可扩展性的长码实际纠错场景
前向纠错对噪声信道中的可靠通信至关重要。基于注意力的无模型神经解码器在短码上表现优异，但其二次方级的内存和计算开销限制了其在长码上的扩展性。本文提出一种无需注意力的、基于校验图的神经解码器MMPD，用于二元线性码。MMPD保留消息传递解码器的Tanner图结构，沿变量-校验边进行局部成对聚合；为实现高效长距离信息传播，将局部更新与双向Mamba状态空间块结合。通过避免稠密注意力矩阵，MMPD在内存和计算上对长码更具可扩展性。在(1056,880) LDPC码上的实验表明，MMPD在指定比特错误率下相较当前最优的CrossMPT解码器获得0.45 dB增益，同时内存消耗降低1.5倍。该优势在更长码上进一步扩大，证明了MMPD在实用长码可扩展神经解码中的适用性。
原文摘要 · Abstract (English)
Forward error correction is essential for reliable communication over noisy channels. Attention-based model-free neural decoders have shown strong performance for short codes, but their scalability to longer codes is limited by the quadratic memory and computational cost of attention. In this paper, we introduce the Mamba message-passing decoder (MMPD), an attention-free syndrome-based neural decoder for binary linear codes. MMPD retains the Tanner-graph structure of a message-passing decoder by performing local pairwise aggregation along variable-check edges. To enable efficient long-range information propagation, these local updates are combined with bidirectional Mamba state-space blocks. By avoiding dense attention matrices, MMPD scales more favorably for long codes in both memory and computation. Experiments on the (1056, 880) LDPC code show that MMPD achieves a 0.45 dB gain over the state-of-the-art CrossMPT decoder at a specified target bit error rate, while reducing memory consumption by a factor of 1.5. This reduction factor increases substantially for longer codes, demonstrating the applicability of MMPD to scalable neural decoding of practical long codes.
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