Automatic Modulation and Recognition of Communication Signals Based on CBAM-GRU
A CBAM-GRU classification model based on the combination of Convolutional Attention Mechanism Module(CBAM)and Gated Recurrent Unit(GRU)network is investigated for automatic modulation identification in non-cooperative com-munication systems.The pre-processed time-domain amplitude,phase and I/Q values of the signal are combined and converted into a matrix of input sample values,which are entered into the network for signal classification and identification.Simulations are con-ducted using the RadioML2016.10a radio dataset,and the CBAM-GRU model are compared with the Convolutional Neural Net-work(CNN),Long Short-Term Memory network(LSTM),GRU,and Convolutional Long Deep Neural Network(CLDNN).The re-sults indicates that the classification accuracy of the CBAM-GRU model reaches 92.79%,showing improvements of 8.52%,1.84%,1.75%,and 8.61%over the comparison models respectively.Compared to traditional CNN or LSTM models,the CBAM-GRU mod-el is more effective in capturing spatio-temporal features of sig-nals,thereby enhancing recognition accuracy.
Automatic modulation recognitionNon-cooperative communication systemsConvolutional block attention mecha-nismGated recurrent unit network
万方数据
维普
