# Data-and-Knowledge Dual-Driven Automatic Modulation Recognition for Wireless Communication Networks

arXiv: https://arxiv.org/abs/2206.15035 · HTML: https://mlsanity.com/paper/2206.15035/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2022.

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

- [Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions](https://mlsanity.com/paper/2503.08091/) (2025)
- [ClST: A Convolutional Transformer Framework for Automatic Modulation Recognition by Knowledge Distillation](https://mlsanity.com/paper/2312.17446/) (2023)
- [MCLRL: A Multi-Domain Contrastive Learning with Reinforcement Learning Framework for Few-Shot Modulation Recognition](https://mlsanity.com/paper/2502.19071/) (2025)

## Abstract (arXiv)

Automatic modulation classification is of crucial importance in wireless communication networks. Deep learning based automatic modulation classification schemes have attracted extensive attention due to the superior accuracy. However, the data-driven method relies on a large amount of training samples and the classification accuracy is poor in the low signal-to-noise radio (SNR). In order to tackle these problems, a novel data-and-knowledge dual-driven automatic modulation classification scheme based on radio frequency machine learning is proposed by exploiting the attribute features of different modulations. The visual model is utilized to extract visual features. The attribute learning model is used to learn the attribute semantic representations. The transformation model is proposed to convert the attribute representation into the visual space. Extensive simulation results demonstrate that our proposed automatic modulation classification scheme can achieve better performance than the benchmark schemes in terms of the classification accuracy, especially in the low SNR. Moreover, the confusion among high-order modulations is reduced by using our proposed scheme compared with other traditional schemes.