# Decoding for Punctured Convolutional and Turbo Codes: A Deep Learning Solution for Protocols Compliance

arXiv: https://arxiv.org/abs/2502.15475 · HTML: https://mlsanity.com/paper/2502.15475/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 55 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 16 of 16)

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- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA](https://mlsanity.com/paper/1612.00694/) (2016)
- [Error Correction Code Transformer](https://mlsanity.com/paper/2203.14966/) (2022)
- [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)
- [Balanced Sparsity for Efficient DNN Inference on GPU](https://mlsanity.com/paper/1811.00206/) (2018)
- [Grow and Prune Compact, Fast, and Accurate LSTMs](https://mlsanity.com/paper/1805.11797/) (2018)
- [Design of a Standard-Compliant Real-Time Neural Receiver for 5G NR](https://mlsanity.com/paper/2409.02912/) (2024)
- [MIND: Model Independent Neural Decoder](https://mlsanity.com/paper/1903.02268/) (2019)
- [DeepTurbo: Deep Turbo Decoder](https://mlsanity.com/paper/1903.02295/) (2019)
- [Deep Learning-based Channel Estimation for Wideband Hybrid MmWave Massive MIMO](https://mlsanity.com/paper/2205.05202/) (2022)
- [SHARP: An Adaptable, Energy-Efficient Accelerator for Recurrent Neural Network](https://mlsanity.com/paper/1911.01258/) (2019)

## Abstract (arXiv)

Neural network-based decoding methods show promise in enhancing error correction performance but face challenges with punctured codes. In particular, existing methods struggle to adapt to variable code rates or meet protocol compatibility requirements. This paper proposes a unified long short-term memory (LSTM)-based neural decoder for punctured convolutional and Turbo codes to address these challenges. The key component of the proposed LSTM-based neural decoder is puncturing-aware embedding, which integrates puncturing patterns directly into the neural network to enable seamless adaptation to different code rates. Moreover, a balanced bit error rate training strategy is designed to ensure the decoder's robustness across various code lengths, rates, and channels. In this way, the protocol compatibility requirement can be realized. Extensive simulations in both additive white Gaussian noise (AWGN) and Rayleigh fading channels demonstrate that the proposed neural decoder outperforms conventional decoding techniques, offering significant improvements in decoding accuracy and robustness.