  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.
