Automatic Modulation Classification Using CNN-LSTM Based Dual-Stream Structure

Deep learning (DL) has recently aroused substantial concern due to its successful implementations in many fields. Currently, there are few studies on the applications of DL in the automatic modulation classification (AMC), which plays a critical role in non-cooperation communications. Besides, most previous work ignores the feature interaction, and only considers spatial or temporal attributes of signals. Combining the advantages of the convolutional neural network (CNN), and the long short-term memory (LSTM), this paper addresses the AMC using CNN-LSTM based dual-stream structure, which efficiently explores the feature interaction, and the spatial-temporal properties of raw complex temporal signals. Specifically, a preprocessing step is first implemented to convert signals into the temporal in-phase/quadrature ($I/Q$) format, and the amplitude/phase ($A/P$) representation, which facilitates the acquirement of more effective features for classification. To extract features from each signal pattern, each stream is composed of CNN, and LSTM (denoted as CNN-LSTM). Most importantly, the features learned from two streams interact in pairs, which increases the diversity of features and thereby further improves the performance. Finally, some comparisons with previous work are performed. The experimental results not only demonstrate the superior performance of the proposed method compared with the existing state-of-the-art methods, but also reveal the potential of DL-based approaches for AMC.