In this work, we investigate the problem of neural-based error correction decoding, and more specifically, the new so-called syndrome-based decoding technique introduced to tackle scalability in the training phase for larger code sizes. We improve on previous works in terms of allowing full decoding of the message rather than codewords, allowing thus the application to non-systematic codes, and proving that the single-message training property is still viable. The suggested system is implemented and tested on polar codes of sizes (64,32) and (128,64), and a BCH of size (63,51), leading to a significant improvement in both Bit Error Rate (BER) and Frame Error Rate (FER), with gains between 0.3dB and 1dB for the implemented codes in the high Signal-to-Noise Ratio (SNR) regime.

翻译：本文研究了基于神经网络的纠错解码问题，特别关注为解决较大码字训练阶段的扩展性问题而引入的新型基于症状的解码技术。相较于先前工作，我们的改进包括：实现消息的完整解码（而非仅解码码字），从而可应用于非系统码，并证明单消息训练属性仍然有效。所提出的系统在(64,32)和(128,64)大小的极化码以及(63,51)大小的BCH码上进行了实现与测试，显著提升了比特误码率（BER）和帧误码率（FER）性能，在高信噪比（SNR）条件下，各实现码型的增益达到0.3dB至1dB。