分析四类神经网络纠错解码器，发现两类无需训练即可达到最优性能。
On the Design and Performance of Machine Learning Based Error Correcting Decoders
- SLNN与MLNN解码器理论上可无条件达到最大似然性能
- 两种基于Transformer的解码器性能不如传统有序统计解码
- 对神经网络纠错解码器在短中码长下的实用性提出质疑
本文分析了四种近期提出的用于前向纠错（FEC）码的神经网络（NN）解码器的设计与竞争力。首先研究了单标签神经网络（SLNN）和多标签神经网络（MLNN）解码器，它们曾报道可实现近似最大似然（ML）性能。我们从理论上证明，无论码长如何，SLNN和MLNN解码器均可始终达到ML性能，尽管计算复杂度较高，且实际上无需训练。随后，我们考察了两种基于Transformer的解码器：纠错码Transformer（ECCT）和交叉注意力消息传递Transformer（CrossMPT），并与传统解码器进行对比，结果表明有序统计解码在性能上优于这些Transformer解码器。本文研究结果对神经网络基FEC解码器在短中码长场景下的实际应用提出了严重质疑。
原文摘要 · Abstract (English)
This paper analyzes the design and competitiveness of four neural network (NN) architectures recently proposed as decoders for forward error correction (FEC) codes. We first consider the so-called single-label neural network (SLNN) and the multi-label neural network (MLNN) decoders which have been reported to achieve near maximum likelihood (ML) performance. Here, we show analytically that SLNN and MLNN decoders can always achieve ML performance, regardless of the code dimensions -- although at the cost of computational complexity -- and no training is in fact required. We then turn our attention to two transformer-based decoders: the error correction code transformer (ECCT) and the cross-attention message passing transformer (CrossMPT). We compare their performance against traditional decoders, and show that ordered statistics decoding outperforms these transformer-based decoders. The results in this paper cast serious doubts on the application of NN-based FEC decoders in the short and medium block length regime.
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