MaskCode：结合线性分组码的反馈辅助编码的掩码Transformer
MaskCode: Mask Transformer for Feedback-Assisted Coding With Linear Block Codes
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中文总结 AI 辅助
针对现有基于机器学习的反馈编码方案对外部码结构不了解的问题，提出MaskCode，一种结合线性分组码的级联编码系统用Transformer反馈码，经评估其在BCH和LDPC外部码上性能优于所有基线，获最高1.5 dB SNR增益。
中文摘要 AI 辅助
基于反馈的编码方案相较于当前的开环编码方案展现出显著的性能提升，遗憾的是，这些增益通常是在具有完美反馈的理想环境下实现的。在过去几年中，基于机器学习的方案已被证明是实现反馈型编码的有前景的解决方案，尤其是在级联编码结构中结合短块长开环纠错码（ECC）时。然而，现有的基于机器学习的反馈方案对外部码的结构并不了解，可能会将反馈资源错误分配给外部ECC已可纠正的错误模式。为解决这一问题，我们提出了MaskCode，一种用于级联编码系统的基于Transformer的内部反馈码，它通过两种协同机制将外部线性分组码的结构知识明确融入内部反馈编码器的设计中：1）基于软伴随式的输入，向编码器告知潜在的奇偶校验约束违反情况；2）由Tanner图导出的感知代码的注意力掩码。我们进一步表明，使用可微分置信传播（BP）解码器进行端到端训练没有额外增益，因为MaskCode的结构感知设计已内置了外部码的结构知识；事实上，通过迭代BP解码器的反向传播会导致梯度爆炸，从而降低而非提升性能。对BCH和LDPC外部码的广泛评估表明，MaskCode始终优于所有基线，实现了高达1.5 dB的信噪比（SNR）增益。
英文摘要
Feedback-based coding schemes have demonstrated substantial performance gains over today's open-loop coding schemes. Unfortunately, these gains are usually achieved in idealized settings with perfect feedback. Over the last few years, machine learning-based schemes have been shown to be promising solutions for implementing feedback-based codes, particularly when combined with short-block-length open-loop error correcting codes (ECCs) in a concatenated coding structure. However, existing ML-based feedback schemes remain agnostic to the outer code's structure, potentially misallocating feedback resources on error patterns already correctable by the outer ECC. To address this, we propose MaskCode, a Transformer-based inner feedback code for concatenated coding systems, which explicitly incorporates structural knowledge of the outer linear block code into the inner feedback encoder design via two synergistic mechanisms: 1) a soft syndrome-based input that informs the encoder about potential parity constraint violations, and 2) a code-aware attention mask derived from the Tanner graph. We further show that end-to-end training with a differentiable belief propagation (BP) decoder offers no additional gain, as MaskCode's structure-aware design already internalizes the structural knowledge of the outer code; in fact, backpropagation through the iterative BP decoder introduces gradient explosion, which degrades rather than improves performance. Extensive evaluations on BCH and LDPC outer codes demonstrate that MaskCode consistently outperforms all baselines, achieving up to 1.5 dB SNR gain.
发表机构
- Chungnam National University（忠南国立大学）
- Purdue University（普渡大学）
- Seoul National University（首尔大学）
机构由 AI 辅助整理，请以论文原文为准。
