Abstract
Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the received signal directly without feature extracion, and it can automatically learn features from the received signals. The features learned by the CNN are presented and analyzed. The robust features of the received signals in a specific SNR range are studied. The accuracy of classification using CNN is shown to be remarkable, particularly for low SNRs. The generalization ability of robust features is also proven to be excellent using the support vector machine (SVM). Finally, to help us better understand the process of feature learning, some outputs of intermediate layers of the CNN are visualized.
Highlights
Automatic modulation classification (AMC) that identifies the modulation type of the received signal is an essential part of noncooperative communication systems
The convolutional neural networks (CNN) can be robust to signal-to-noise ratio (SNR) variation; they can be deployed in a certain SNR range
3.4 Visualization of feature learning process We have proven that the CNN can learn efficient features for classification
Summary
Automatic modulation classification (AMC) that identifies the modulation type of the received signal is an essential part of noncooperative communication systems. Zhu and Fujii proposed a high-accuracy classification scenario [20], where 10 different HOC features were extracted from 5 modulation types, and SDAE was used to classify these features. O’Shea et al [18] trained the CNN with the received based band signals directly, and the classification accuracy was higher than those trained by HOC features. Current schemes are often lack of generalization ability To solve this problem, a CNN-SVM model for AMC is proposed in this paper. As long as the SNR range of the communication channel is known, the CNN can learn the features that adapt to the corresponding condition This property makes our method independent from the SNR estimation. We replace the fully connected layer with a global average pooling layer, so that there is no fully connected layer
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