Small neural networks (NNs) used for error correction were shown to improve on classic channel codes and to address channel model changes. We extend the code dimension of any such structure by using the same NN under one-hot encoding multiple times, then serially-concatenated with an outer classic code. We design NNs with the same network parameters, where each Reed-Solomon codeword symbol is an input to a different NN. Significant improvements in block error probabilities for an additive Gaussian noise channel as compared to the small neural code are illustrated, as well as robustness to channel model changes.

翻译：摘要：研究表明，用于纠错的小型神经网络（NN）能够改善经典信道编码性能并适应信道模型变化。本文通过在同一神经网络上多次应用独热编码，随后与外部经典码进行串行级联，扩展了此类结构的码字维度。我们设计了具有相同网络参数的神经网络，每个里德-所罗门码字符号作为不同神经网络的输入。相较于小型神经编码，本文方法在高斯加性噪声信道上显著降低了分组错误概率，并展现出对信道模型变化的鲁棒性。