# Communication Algorithms via Deep Learning

arXiv: https://arxiv.org/abs/1805.09317 · HTML: https://mlsanity.com/paper/1805.09317/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2018.

## Built on by (most cited first, 30 of 42)

- [Learning to Optimize: A Primer and A Benchmark](https://mlsanity.com/paper/2103.12828/) (2021)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Error Correction Code Transformer](https://mlsanity.com/paper/2203.14966/) (2022)
- [Denoising Diffusion Error Correction Codes](https://mlsanity.com/paper/2209.13533/) (2022)
- [CrossMPT: Cross-attention Message-Passing Transformer for Error Correcting Codes](https://mlsanity.com/paper/2405.01033/) (2024)
- [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)
- [Factor Graph Neural Networks](https://mlsanity.com/paper/2308.00887/) (2023)
- [Cyclically Equivariant Neural Decoders for Cyclic Codes](https://mlsanity.com/paper/2105.05540/) (2021)
- [Deep Quantum Error Correction](https://mlsanity.com/paper/2301.11930/) (2023)
- [Learning Linear Block Error Correction Codes](https://mlsanity.com/paper/2405.04050/) (2024)
- [A Channel Coding Benchmark for Meta-Learning](https://mlsanity.com/paper/2107.07579/) (2021)
- [Neural Network-based OFDM Receiver for Resource Constrained IoT Devices](https://mlsanity.com/paper/2205.06159/) (2022)
- [CRISP: Curriculum based Sequential Neural Decoders for Polar Code Family](https://mlsanity.com/paper/2210.00313/) (2022)
- [How to Mask in Error Correction Code Transformer: Systematic and Double Masking](https://mlsanity.com/paper/2308.08128/) (2023)
- [Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning](https://mlsanity.com/paper/2304.13178/) (2023)
- [Hybrid Mamba-Transformer Decoder for Error-Correcting Codes](https://mlsanity.com/paper/2505.17834/) (2025)
- [TinyTurbo: Efficient Turbo Decoders on Edge](https://mlsanity.com/paper/2209.15614/) (2022)
- [Factor Graph Optimization of Error-Correcting Codes for Belief Propagation Decoding](https://mlsanity.com/paper/2406.12900/) (2024)
- [Accelerating Error Correction Code Transformers](https://mlsanity.com/paper/2410.05911/) (2024)
- [Nested Construction of Polar Codes via Transformers](https://mlsanity.com/paper/2401.17188/) (2024)
- [MinGRU-Based Encoder for Turbo Autoencoder Frameworks](https://mlsanity.com/paper/2503.08451/) (2025)
- [Improving the List Decoding Version of the Cyclically Equivariant Neural Decoder](https://mlsanity.com/paper/2106.07964/) (2021)
- [PRONTO: Preamble Overhead Reduction with Neural Networks for Coarse Synchronization](https://mlsanity.com/paper/2112.10885/) (2021)
- [List Autoencoder: Towards Deep Learning Based Reliable Transmission Over Noisy Channels](https://mlsanity.com/paper/2112.11920/) (2021)
- [Deep Learning-Based Detection for Marker Codes over Insertion and Deletion Channels](https://mlsanity.com/paper/2401.01155/) (2024)
- [Friendly Attacks to Improve Channel Coding Reliability](https://mlsanity.com/paper/2401.14184/) (2024)
- [DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning](https://mlsanity.com/paper/2402.08864/) (2024)
- [Erasure Coded Neural Network Inference via Fisher Averaging](https://mlsanity.com/paper/2409.01420/) (2024)
- [Deep Learning in Wireless Communication Receiver: A Survey](https://mlsanity.com/paper/2501.17184/) (2025)

## Abstract (arXiv)

Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the discovery of decoding algorithms via deep learning. We study a family of sequential codes parameterized by recurrent neural network (RNN) architectures. We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms). We show strong generalizations, i.e., we train at a specific signal to noise ratio and block length but test at a wide range of these quantities, as well as robustness and adaptivity to deviations from the AWGN setting.