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.

翻译：自动调制分类（AMC）是无线通信系统中频谱管理、信号监测与控制的关键步骤。调制格式的精确分类对后续传输数据的解码至关重要。端到端深度学习方法近年来已被应用于AMC，其性能优于传统特征工程技术。然而，AMC在低信噪比（SNR）环境中仍存在局限性。为解决这一缺陷，我们提出了一种新颖的AMC-Net，通过在频域中对输入信号进行降噪处理，同时执行多尺度与高效特征提取，从而提升识别能力。在两个代表性数据集上的实验表明，我们的模型在效率和有效性方面均优于当前大多数方法。