# Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels

arXiv: https://arxiv.org/abs/1911.03038 · HTML: https://mlsanity.com/paper/1911.03038/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2019.

## Built on by (most cited first, 30 of 44)

- [KO codes: Inventing Nonlinear Encoding and Decoding for Reliable Wireless Communication via Deep-learning](https://mlsanity.com/paper/2108.12920/) (2021)
- [Autoregressive Belief Propagation for Decoding Block Codes](https://mlsanity.com/paper/2103.11780/) (2021)
- [ProductAE: Towards Training Larger Channel Codes based on Neural Product Codes](https://mlsanity.com/paper/2110.04466/) (2021)
- [Neural Distributed Source Coding](https://mlsanity.com/paper/2106.02797/) (2021)
- [Cyclically Equivariant Neural Decoders for Cyclic Codes](https://mlsanity.com/paper/2105.05540/) (2021)
- [Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues](https://mlsanity.com/paper/2303.08106/) (2023)
- [Learning Linear Block Error Correction Codes](https://mlsanity.com/paper/2405.04050/) (2024)
- [SVD-Embedded Deep Autoencoder for MIMO Communications](https://mlsanity.com/paper/2111.02359/) (2021)
- [Concatenated Classic and Neural (CCN) Codes: ConcatenatedAE](https://mlsanity.com/paper/2209.01701/) (2022)
- [CRISP: Curriculum based Sequential Neural Decoders for Polar Code Family](https://mlsanity.com/paper/2210.00313/) (2022)
- [Diffusion Models for Accurate Channel Distribution Generation](https://mlsanity.com/paper/2309.10505/) (2023)
- [Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning](https://mlsanity.com/paper/2304.13178/) (2023)
- [Generalization Bounds for Neural Belief Propagation Decoders](https://mlsanity.com/paper/2305.10540/) (2023)
- [Factor Graph Optimization of Error-Correcting Codes for Belief Propagation Decoding](https://mlsanity.com/paper/2406.12900/) (2024)
- [End-to-End Learning for Uplink MU-SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channels](https://mlsanity.com/paper/2105.01260/) (2021)
- [Realizing Neural Decoder at the Edge with Ensembled BNN](https://mlsanity.com/paper/2106.09925/) (2021)
- [DeepIC: Coding for Interference Channels via Deep Learning](https://mlsanity.com/paper/2108.06028/) (2021)
- [Hybrid Neural Coded Modulation: Design and Training Methods](https://mlsanity.com/paper/2202.01972/) (2022)
- [MinGRU-Based Encoder for Turbo Autoencoder Frameworks](https://mlsanity.com/paper/2503.08451/) (2025)
- [Generalization Bounds for Transformer Channel Decoders](https://mlsanity.com/paper/2601.06969/) (2026)
- [Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding](https://mlsanity.com/paper/2102.03959/) (2021)
- [Improving the List Decoding Version of the Cyclically Equivariant Neural Decoder](https://mlsanity.com/paper/2106.07964/) (2021)
- [Discriminative Mutual Information Estimators for Channel Capacity Learning](https://mlsanity.com/paper/2107.03084/) (2021)
- [List Autoencoder: Towards Deep Learning Based Reliable Transmission Over Noisy Channels](https://mlsanity.com/paper/2112.11920/) (2021)
- [RELDEC: Reinforcement Learning-Based Decoding of Moderate Length LDPC Codes](https://mlsanity.com/paper/2112.13934/) (2021)
- [Optimizing Serially Concatenated Neural Codes with Classical Decoders](https://mlsanity.com/paper/2212.10355/) (2022)
- [Machine Learning-Aided Efficient Decoding of Reed-Muller Subcodes](https://mlsanity.com/paper/2301.06251/) (2023)
- [ProductAE: Toward Deep Learning Driven Error-Correction Codes of Large Dimensions](https://mlsanity.com/paper/2303.16424/) (2023)
- [Component Training of Turbo Autoencoders](https://mlsanity.com/paper/2305.09216/) (2023)
- [Quantum Autoencoders for Learning Quantum Channel Codes](https://mlsanity.com/paper/2307.06622/) (2023)

## Abstract (arXiv)

Designing codes that combat the noise in a communication medium has remained a significant area of research in information theory as well as wireless communications. Asymptotically optimal channel codes have been developed by mathematicians for communicating under canonical models after over 60 years of research. On the other hand, in many non-canonical channel settings, optimal codes do not exist and the codes designed for canonical models are adapted via heuristics to these channels and are thus not guaranteed to be optimal. In this work, we make significant progress on this problem by designing a fully end-to-end jointly trained neural encoder and decoder, namely, Turbo Autoencoder (TurboAE), with the following contributions: (a) under moderate block lengths, TurboAE approaches state-of-the-art performance under canonical channels; (b) moreover, TurboAE outperforms the state-of-the-art codes under non-canonical settings in terms of reliability. TurboAE shows that the development of channel coding design can be automated via deep learning, with near-optimal performance.