# TinyTurbo: Efficient Turbo Decoders on Edge

arXiv: https://arxiv.org/abs/2209.15614 · HTML: https://mlsanity.com/paper/2209.15614/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [Learning to Decode Linear Codes Using Deep Learning](https://mlsanity.com/paper/1607.04793/) (2016)
- [Communication Algorithms via Deep Learning](https://mlsanity.com/paper/1805.09317/) (2018)
- [An Iterative BP-CNN Architecture for Channel Decoding](https://mlsanity.com/paper/1707.05697/) (2017)
- [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 Learning-based Polar Code Design](https://mlsanity.com/paper/1909.12035/) (2019)
- [Pruning Neural Belief Propagation Decoders](https://mlsanity.com/paper/2001.07464/) (2020)
- [LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks](https://mlsanity.com/paper/1811.12707/) (2018)
- [DeepTurbo: Deep Turbo Decoder](https://mlsanity.com/paper/1903.02295/) (2019)
- [Low-complexity Recurrent Neural Network-based Polar Decoder with Weight Quantization Mechanism](https://mlsanity.com/paper/1810.12154/) (2018)
- [Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results](https://mlsanity.com/paper/2006.08896/) (2020)

## Built on by (most cited first, 3 of 3)

- [CRISP: Curriculum based Sequential Neural Decoders for Polar Code Family](https://mlsanity.com/paper/2210.00313/) (2022)
- [LightCode: Light Analytical and Neural Codes for Channels with Feedback](https://mlsanity.com/paper/2403.10751/) (2024)
- [Nested Construction of Polar Codes via Transformers](https://mlsanity.com/paper/2401.17188/) (2024)

## Abstract (arXiv)

In this paper, we introduce a neural-augmented decoder for Turbo codes called TINYTURBO . TINYTURBO has complexity comparable to the classical max-log-MAP algorithm but has much better reliability than the max-log-MAP baseline and performs close to the MAP algorithm. We show that TINYTURBO exhibits strong robustness on a variety of practical channels of interest, such as EPA and EVA channels, which are included in the LTE standards. We also show that TINYTURBO strongly generalizes across different rate, blocklengths, and trellises. We verify the reliability and efficiency of TINYTURBO via over-the-air experiments.