A Convolutional and Transformer Based Deep Neural Network for Automatic Modulation Classification

Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel data-driven framework named convolutional and transformer-based deep neural network(CTDNN)is proposed to improve the classification performance.CTDNN can be divided into four modules,i.e.,convolutional neural network(CNN)backbone,transition module,transformer module,and final classifier.In the CNN backbone,a wide and deep convolution structure is designed,which consists of 1×15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential connections.In the transition module,a 1×1 convolution layer is utilized to compress the channels of the previous multi-scale CNN features.In the transformer module,three self-attention layers are designed for extracting global features and generating the classification vector.In the classifier,the final decision is made based on the maximum a posterior probability.Extensive simulations are conducted,and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.

Hierarchical Digital Modulation Classification Using Cascaded Convolutional Neural Network

Automatic modulation classification(AMC)aims to identify the modulation format of the received signals corrupted by the noise,which plays a major role in radio monitoring.In this paper,we propose a novel cascaded convolutional neural network(CasCNN)-based hierarchical digital modulation classification scheme,where M-ary phase shift keying(PSK)and M-ary quadrature amplitude modulation(QAM)modulation formats are considered to be classified.In CasCNN,two-block convolutional neural networks are cascaded.The first block network is utilized to classify the different classes of modulation formats,namely PSK and QAM.The second block is designed to identify the indexes of the modulations in the same PSK or QAM class.Moreover,it is noted that the gird constellation diagram extracted from the received signal is utilized as the inputs to the CasCNN.Extensive simulations demonstrate that CasCNN yields performance gain and performs stronger robustness to frequency offset compared with other recent methods.Specifically,CasCNN achieves 90%classification accuracy at 4 dB signal-to-noise ratio when the symbol length is set as 256.