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解码问题，特别针对为解决较大码字训练阶段可扩展性问题而提出的新型基于综合征的解码技术。我们在先前工作的基础上进行了改进，实现了对完整消息而非码字的解码，从而可应用于非系统码，并证明了单消息训练特性的有效性。所提系统在(64,32)和(128,64)尺寸的极化码以及(63,51)尺寸的BCH码上进行了实现与测试。实验结果表明，在高信噪比区域，所实现码字的误比特率和误帧率均获得显著改善，增益范围为0.3dB至1dB。