Considering the low classification accuracy of spreading and conventional modulated signals under low signal-to-noise ratio conditions, a multimodal attention mechanism signal modulation recognition method based on Generative Adversarial Network (GAN) and Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) network is proposed. Firstly, the Time-Frequency Images (TFIs) of the to-be-recognized signals are generated and the noise reduction process of TFIs is realized by using GAN; Secondly, the In-phase and Quadrature data (I/Q data) of the signals with TFIs are used as model inputs, and the CNN-based TFIs recognition branch and the LSTM-based I/Q data recognition branch are built; Finally, an attentional mechanism is added to the model to enhance the role of important features in I/Q data and TFIs in the determination of classification results. The experimental results show that the proposed method effectively improves the overall classification accuracy by 2％ to 7％ compared with the unimodal recognition model and other baseline models, and possesses stronger feature expression capability and robustness under low signal-to-noise ratio conditions.
关键词
深度学习/自动调制识别/生成对抗网络(GAN)/多模态特征/时频分布
Key words
Deep learning/Automatic Modulation Recognition(AMR)/Generate Adversarial Network(GAN)/Multi-modal features/Time-frequency distribution
维普
万方数据