级联经典与神经（CCN）码：ConcatenatedAE
Concatenated Classic and Neural (CCN) Codes: ConcatenatedAE
AI总结：
提出ConcatenatedAE方法，通过在独热编码下多次复用相同NN并串行级联外部经典码来扩展码维度，显著降低了加性高斯噪声信道下的块错误概率并增强了对信道模型变化的鲁棒性。
摘要：
用于纠错的小型神经网络（NN）被证明能改进经典信道码并应对信道模型变化。我们通过在独热编码下多次使用相同的NN来扩展任何此类结构的码维度，然后与外部经典码进行串行级联。我们设计了具有相同网络参数的NN，其中每个Reed-Solomon码字符号是不同NN的输入。结果表明，与小型神经码相比，在加性高斯噪声信道下的块错误概率有显著改善，并且对信道模型变化具有鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
