# Concatenated Classic and Neural (CCN) Codes: ConcatenatedAE

arXiv: https://arxiv.org/abs/2209.01701 · HTML: https://mlsanity.com/paper/2209.01701/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 4 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels](https://mlsanity.com/paper/1911.03038/) (2019)
- [Trainable Communication Systems: Concepts and Prototype](https://mlsanity.com/paper/1911.13055/) (2019)
- [KO codes: Inventing Nonlinear Encoding and Decoding for Reliable Wireless Communication via Deep-learning](https://mlsanity.com/paper/2108.12920/) (2021)
- [ProductAE: Towards Training Larger Channel Codes based on Neural Product Codes](https://mlsanity.com/paper/2110.04466/) (2021)
- [Hybrid Neural Coded Modulation: Design and Training Methods](https://mlsanity.com/paper/2202.01972/) (2022)

## Built on by (most cited first, 5 of 5)

- [MinGRU-Based Encoder for Turbo Autoencoder Frameworks](https://mlsanity.com/paper/2503.08451/) (2025)
- [Deep Randomized Distributed Function Computation (DeepRDFC): Neural Distributed Channel Simulation](https://mlsanity.com/paper/2603.10750/) (2026)
- [Modular Neural Wiretap Codes for Fading Channels](https://mlsanity.com/paper/2409.08786/) (2024)
- [Forward-Forward Autoencoder Architectures for Energy-Efficient Wireless Communications](https://mlsanity.com/paper/2510.11418/) (2025)
- [Short-Length Code Designs for Integrated Sensing and Communications: A Deep Learning Approach](https://mlsanity.com/paper/2609.33605/) (2026)

## Abstract (arXiv)

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.