# Joint Neural Network Equalizer and Decoder

arXiv: https://arxiv.org/abs/1807.02040 · HTML: https://mlsanity.com/paper/1807.02040/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Hybrid HMM Decoder For Convolutional Codes By Joint Trellis-Like Structure and Channel Prior](https://mlsanity.com/paper/2210.14749/) (2022)

## Abstract (arXiv)

Recently, deep learning methods have shown significant improvements in communication systems. In this paper, we study the equalization problem over the nonlinear channel using neural networks. The joint equalizer and decoder based on neural networks are proposed to realize blind equalization and decoding process without the knowledge of channel state information (CSI). Different from previous methods, we use two neural networks instead of one. First, convolutional neural network (CNN) is used to adaptively recover the transmitted signal from channel impairment and nonlinear distortions. Then the deep neural network decoder (NND) decodes the detected signal from CNN equalizer. Under various channel conditions, the experiment results demonstrate that the proposed CNN equalizer achieves better performance than other solutions based on machine learning methods. The proposed model reduces about 2/3 of the parameters compared to state-of-the-art counterparts. Besides, our model can be easily applied to long sequence with O(n) complexity.