A novel attention cooperative framework for automatic modulation recognition

Modulation recognition plays an indispensable role in the field of wireless communications. In this paper, a novel attention cooperative framework based on deep learning is proposed to improve the accuracy of the automatic modulation recognition (AMR). Within this framework, a convolutional neural network (CNN), a recurrent neural network (RNN), and a generative adversarial network (GAN) are constructed to cooperate in AMR. A cyclic connected CNN (CCNN) is designed to extract spatial features of the received signal, and a bidirectional RNN (BRNN) is constructed for obtaining temporal features. To take full advantage of the complementarity and relevance between the spatial and temporal features, a fusion strategy based on global average and max pooling (GAMP) is proposed. To deal with different influence levels of the signal feature maps, we present the attention mechanism in this framework to realize recalibration. Besides, modulation recognition based on deep learning requires numerous data for training purposes, which is difficult to achieve in practical AMR applications. Therefore, an auxiliary classification GAN (ACGAN) is developed as a generator to expand the training set, and we modify the loss function of ACGAN to accommodate the processing of the actual in-phase and quadrature (I/Q) signal data. Considering the difference in distribution between generated data and real data, we propose a novel auxiliary weighing loss function to achieve higher recognition accuracy. Experimental results on the dataset RML2016.10a show that the proposed framework outperforms existing deep learning-based approaches and achieves 94% accuracy at high signal to noise ratio (SNR).