Robust Automatic Modulation Classification Under Varying Noise Conditions

Automatic modulation classification (AMC) plays a key role in non-cooperative communication systems. Feature-based (FB) methods have been widely studied in particular. Most existing FB methods are deployed at a fixed SNR level, and the pre-trained classifiers may no longer be effective when the SNR level changes. The classifiers may also need to be re-trained to be suitable for the varying channel environment. To address these problems, a robust AMC method under varying noise conditions is proposed in this paper. The method attempts to select noise-insensitive features from a large feature set to ensure that the trained classifiers will be robust to SNR variations. First, a feature set consisting of 25 types of features is extracted, and 4 features that are insensitive to noise are chosen through a feature selection method based on rough set theory. The generalizability of an SVM classifier trained on the 4 chosen features is evaluated based on numerical results. The classification accuracy remains reasonable when the SNR varies between 5 and 20 dB, indicating that the proposed method can be deployed under varying noise conditions.