  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.
