Scalable Mamba-Based Message-Passing Neural Decoder for Error-Correcting Codes

TL;DR

This paper introduces MMPD, an attention-free neural decoder for error-correcting codes that scales efficiently to long codes by combining message-passing with bidirectional Mamba state-space blocks.

Contribution

The paper presents MMPD, a novel scalable neural decoder that avoids attention matrices, enabling efficient decoding of long codes with improved performance and reduced memory usage.

Findings

MMPD achieves a 0.45 dB gain over CrossMPT on a (1056, 880) LDPC code.

MMPD reduces memory consumption by a factor of 1.5 compared to state-of-the-art decoders.

The scalability of MMPD increases with longer codes, demonstrating practical applicability.

Abstract

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.