ABSTRACT 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.
Modulation classification system under varying noise conditions is proposed in REF that ensure robustness to SNR variations.
Robust Automatic Modulation Classification Under Varying Noise Conditions
IEEE Access
IEEE Access
Computer Science