论文标题
串联经典和神经（CCN）代码：condenatedae
Concatenated Classic and Neural (CCN) Codes: ConcatenatedAE
论文作者
论文摘要
显示用于误差校正的小型神经网络（NNS）可改善经典通道代码并解决通道模型的更改。我们通过在单速编码下多次使用相同的NN扩展任何此类结构的代码维度，然后与外部经典代码串行汇用。我们设计具有相同网络参数的NN，其中每个REED - Solomon CodeWord符号都是对其他NN的输入。说明了与小神经代码相比，加斯高斯噪声通道的块误差概率的显着改善，以及通道模型变化的鲁棒性。
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.
