Scalable Mamba-Based Message-Passing Neural Decoder for Error-Correcting Codes
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 bloc...
| Authors | Rostislav Gusev, Nikita Aleksandrov, Artem Solomkin et al. |
|---|---|
| Primary category | cs.IT |
| Published | 11 May 2026 |
| Last updated at source | 19 September 2026 |
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