AMC-Net: An Effective Network for Automatic Modulation Classification

• URL: http://arxiv.org/abs/2304.00445v1
• Date: Sun, 2 Apr 2023 04:26:30 GMT
• Title: AMC-Net: An Effective Network for Automatic Modulation Classification
• Authors: Jiawei Zhang, Tiantian Wang, Zhixi Feng, Shuyuan Yang
• Abstract summary: 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.
• Score: 22.871024969842335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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