# Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning

arXiv: https://arxiv.org/abs/2304.13178 · HTML: https://mlsanity.com/paper/2304.13178/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 12 of 12)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Deep Learning-Based Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [Learning to Decode Linear Codes Using Deep Learning](https://mlsanity.com/paper/1607.04793/) (2016)
- [Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels](https://mlsanity.com/paper/1911.03038/) (2019)
- [Communication Algorithms via Deep Learning](https://mlsanity.com/paper/1805.09317/) (2018)
- [Deepcode: Feedback Codes via Deep Learning](https://mlsanity.com/paper/1807.00801/) (2018)
- [KO codes: Inventing Nonlinear Encoding and Decoding for Reliable Wireless Communication via Deep-learning](https://mlsanity.com/paper/2108.12920/) (2021)
- [Cyclically Equivariant Neural Decoders for Cyclic Codes](https://mlsanity.com/paper/2105.05540/) (2021)
- [Deep Extended Feedback Codes](https://mlsanity.com/paper/2105.01365/) (2021)
- [All you need is feedback: Communication with block attention feedback codes](https://mlsanity.com/paper/2206.09457/) (2022)
- [Linear Coding for AWGN channels with Noisy Output Feedback via Dynamic Programming](https://mlsanity.com/paper/2103.10807/) (2021)

## Built on by (most cited first, 4 of 4)

- [LightCode: Light Analytical and Neural Codes for Channels with Feedback](https://mlsanity.com/paper/2403.10751/) (2024)
- [Coding for Gaussian Two-Way Channels: Linear and Learning-Based Approaches](https://mlsanity.com/paper/2401.00477/) (2023)
- [DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning](https://mlsanity.com/paper/2402.08864/) (2024)
- [MaskCode: Mask Transformer for Feedback-Assisted Coding With Linear Block Codes](https://mlsanity.com/paper/2609.00715/) (2026)

## Abstract (arXiv)

The design of codes for feedback-enabled communications has been a long-standing open problem. Recent research on non-linear, deep learning-based coding schemes have demonstrated significant improvements in communication reliability over linear codes, but are still vulnerable to the presence of forward and feedback noise over the channel. In this paper, we develop a new family of non-linear feedback codes that greatly enhance robustness to channel noise. Our autoencoder-based architecture is designed to learn codes based on consecutive blocks of bits, which obtains de-noising advantages over bit-by-bit processing to help overcome the physical separation between the encoder and decoder over a noisy channel. Moreover, we develop a power control layer at the encoder to explicitly incorporate hardware constraints into the learning optimization, and prove that the resulting average power constraint is satisfied asymptotically. Numerical experiments demonstrate that our scheme outperforms state-of-the-art feedback codes by wide margins over practical forward and feedback noise regimes, and provide information-theoretic insights on the behavior of our non-linear codes. Moreover, we observe that, in a long blocklength regime, canonical error correction codes are still preferable to feedback codes when the feedback noise becomes high.