# AMC-Net: An Effective Network for Automatic Modulation Classification

arXiv: https://arxiv.org/abs/2304.00445 · HTML: https://mlsanity.com/paper/2304.00445/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 2 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors](https://mlsanity.com/paper/1707.08908/) (2017)
- [Deep Architectures for Modulation Recognition](https://mlsanity.com/paper/1703.09197/) (2017)
- [Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges](https://mlsanity.com/paper/2207.09647/) (2022)
- [Frequency-based Automated Modulation Classification in the Presence of Adversaries](https://mlsanity.com/paper/2011.01132/) (2020)
- [Applications of Deep Learning to the Design of Enhanced Wireless Communication Systems](https://mlsanity.com/paper/2205.01210/) (2022)

## Built on by (most cited first, 9 of 9)

- [SpectrumFM: A Foundation Model for Intelligent Spectrum Management](https://mlsanity.com/paper/2505.06256/) (2025)
- [FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition](https://mlsanity.com/paper/2505.21571/) (2025)
- [EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding](https://mlsanity.com/paper/2508.18785/) (2025)
- [AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities](https://mlsanity.com/paper/2502.05315/) (2025)
- [DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning](https://mlsanity.com/paper/2507.12011/) (2025)
- [T-PRIME: Transformer-based Protocol Identification for Machine-learning at the Edge](https://mlsanity.com/paper/2401.04837/) (2024)
- [ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditions](https://mlsanity.com/paper/2503.18375/) (2025)
- [SpectrumFM: Redefining Spectrum Cognition via Foundation Modeling](https://mlsanity.com/paper/2508.02742/) (2025)
- [End-to-End Radar and Communication Modulation Recognition with Neuromorphic Computing](https://mlsanity.com/paper/2606.24075/) (2026)

## Abstract (arXiv)

Automatic modulation classification (AMC) is a crucial stage in the spectrum management, signal monitoring, and control of wireless communication systems. The accurate classification of the modulation format plays a vital role in the subsequent decoding of the transmitted data. End-to-end deep learning methods have been recently applied to AMC, outperforming traditional feature engineering techniques. However, AMC still has limitations in low signal-to-noise ratio (SNR) environments. To address the drawback, we propose a novel AMC-Net that improves recognition by denoising the input signal in the frequency domain while performing multi-scale and effective feature extraction. Experiments on two representative datasets demonstrate that our model performs better in efficiency and effectiveness than the most current methods.