# MinGRU-Based Encoder for Turbo Autoencoder Frameworks

arXiv: https://arxiv.org/abs/2503.08451 · HTML: https://mlsanity.com/paper/2503.08451/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 11 Mar 2025.

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

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Resurrecting Recurrent Neural Networks for Long Sequences](https://mlsanity.com/paper/2303.06349/) (2023)
- [xLSTM: Extended Long Short-Term Memory](https://mlsanity.com/paper/2405.04517/) (2024)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [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)
- [Were RNNs All We Needed?](https://mlsanity.com/paper/2410.01201/) (2024)
- [ProductAE: Towards Training Larger Channel Codes based on Neural Product Codes](https://mlsanity.com/paper/2110.04466/) (2021)
- [DeepTurbo: Deep Turbo Decoder](https://mlsanity.com/paper/1903.02295/) (2019)
- [Concatenated Classic and Neural (CCN) Codes: ConcatenatedAE](https://mlsanity.com/paper/2209.01701/) (2022)

## Built on by (most cited first, 2 of 2)

- [Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning](https://mlsanity.com/paper/2603.03229/) (2026)
- [Short-Length Code Designs for Integrated Sensing and Communications: A Deep Learning Approach](https://mlsanity.com/paper/2609.33605/) (2026)

## Abstract (arXiv)

Early neural channel coding approaches leveraged dense neural networks with one-hot encodings to design adaptive encoder-decoder pairs, improving block error rate (BLER) and automating the design process. However, these methods struggled with scalability as the size of message sets and block lengths increased. TurboAE addressed this challenge by focusing on bit-sequence inputs rather than symbol-level representations, transforming the scalability issue associated with large message sets into a sequence modeling problem. While recurrent neural networks (RNNs) were a natural fit for sequence processing, their reliance on sequential computations made them computationally expensive and inefficient for long sequences. As a result, TurboAE adopted convolutional network blocks, which were faster to train and more scalable, but lacked the sequential modeling advantages of RNNs. Recent advances in efficient RNN architectures, such as minGRU and minLSTM, and structured state space models (SSMs) like S4 and S6, overcome these limitations by significantly reducing memory and computational overhead. These models enable scalable sequence processing, making RNNs competitive for long-sequence tasks. In this work, we revisit RNNs for Turbo autoencoders by integrating the lightweight minGRU model with a Mamba block from SSMs into a parallel Turbo autoencoder framework. Our results demonstrate that this hybrid design matches the performance of convolutional network-based Turbo autoencoder approaches for short sequences while significantly improving scalability and training efficiency for long block lengths. This highlights the potential of efficient RNNs in advancing neural channel coding for long-sequence scenarios.