# Performance Evaluation of Channel Decoding With Deep Neural Networks

arXiv: https://arxiv.org/abs/1711.00727 · HTML: https://mlsanity.com/paper/1711.00727/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2017.

## Built on by (most cited first, 7 of 7)

- [CRISP: Curriculum based Sequential Neural Decoders for Polar Code Family](https://mlsanity.com/paper/2210.00313/) (2022)
- [The Next Decade of Telecommunications Artificial Intelligence](https://mlsanity.com/paper/2101.09163/) (2021)
- [Digital Signal Processing Using Deep Neural Networks](https://mlsanity.com/paper/2109.10404/) (2021)
- [Deep Learning for Spectral Filling in Radio Frequency Applications](https://mlsanity.com/paper/2204.01536/) (2022)
- [Neural Network Decoders for Permutation Codes Correcting Different Errors](https://mlsanity.com/paper/2206.03315/) (2022)
- [NNCTC: Physical Layer Cross-Technology Communication via Neural Networks](https://mlsanity.com/paper/2403.10014/) (2024)
- [Score Based Error Correcting Code Decoder](https://mlsanity.com/paper/2605.28358/) (2026)

## Abstract (arXiv)

With the demand of high data rate and low latency in fifth generation (5G), deep neural network decoder (NND) has become a promising candidate due to its capability of one-shot decoding and parallel computing. In this paper, three types of NND, i.e., multi-layer perceptron (MLP), convolution neural network (CNN) and recurrent neural network (RNN), are proposed with the same parameter magnitude. The performance of these deep neural networks are evaluated through extensive simulation. Numerical results show that RNN has the best decoding performance, yet at the price of the highest computational overhead. Moreover, we find there exists a saturation length for each type of neural network, which is caused by their restricted learning abilities.