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：本文研究基于神经网络的纠错译码问题，特别关注新提出的基于综合征的译码技术——该技术旨在解决较大码长训练阶段的扩展性问题。我们改进了以往的工作，实现了对完整消息而非码字的译码，从而可应用于非系统码，并证明了单消息训练特性仍然可行。所提出的系统在(64,32)和(128,64)规模的极化码以及(63,51)规模的BCH码上进行了实现和测试，误码率(BER)和误帧率(FER)均获得显著提升，在高信噪比(SNR)区域，实现的码字可获得0.3dB至1dB的增益。