Spread Spectrum and Conventional Modulation Signal Recognition Method Based on Generative Adversarial Network and Multi-modal Attention Mechanism
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.
Deep learningAutomatic Modulation Recognition(AMR)Generate Adversarial Network(GAN)Multi-modal featuresTime-frequency distribution
万方数据
维普
