Research On Modulation Identification Method Of Communication Signal Under Low Signal-to-noise Ratio

Modulation and identification of communication signals has important research significance in both military and civilian fields.In the absence of prior information,it is critical to accurately identify the modulation mode of the received signal for subsequent operations and processing at the receiving end.The traditional modulation recognition method requires artificial design,screening and extraction of features,and the extracted features have poor generalization ability.identify.Based on this,this paper combines signal processing with deep learning to effectively improve the modulation recognition effect of communication signals under low signal-to-noise ratio.Firstly,this paper studies the principle of communication signal modulation,signal time-frequency characterization method and related theories of deep learning,and introduces in detail the nonlinear time-frequency analysis theory and the network structure of convolutional neural network(CNN),commonly used optimization algorithms and activation functions,which lays a theoretical foundation for the subsequent modulation recognition algorithm based on the combination of signal time-frequency representation and convolutional neural network.Then,five CNN structures are designed,and the time-frequency map data set is generated by the smooth pseudo-Wigner time-frequency distribution for network training and testing,and the optimal network—the feature channel series network is determined through experimental comparison,which is the network structure in the subsequent chapters.The choice lays the foundation.Next,in view of the problem that the noise in the time-frequency diagram of the smooth pseudo-Wigner distribution seriously interferes with the characteristics of the useful signal under the low signal-to-noise ratio,a high-order time-frequency analysis method—Wigner fourth-order moment spectrum is used as the time-frequency representation method of the signal.The slicing operation realizes dimensionality reduction.The fuzzy domain kernel function filtering method is used to remove the cross terms on the time-frequency surface,and the time-frequency aggregation of the high-order distribution is analyzed.The time-frequency distribution has a better ability to highlight useful signal features;in order to further improve the network recognition performance,an ECA attention mechanism is added to the network;in order to reduce the parameter content and computational complexity,lightweight improvements are achieved.Finally,also for the problem that the time-frequency graph of smooth pseudo-Wigner distribution is seriously disturbed by noise,a time-frequency graph denoising network based on residual learning and a time-frequency graph denoising network based on lightweight residual attention UNet are designed.Two kinds of time-frequency map denoising networks are used to denoise the noisy time-frequency map data set,and two kinds of denoising time-frequency map data sets are generated,which are used to train the feature channel series-ECA network to verify the improvement of the algorithm on the recognition effect.and compare the denoising performance of the two denoising networks.The experimental results show that while keeping the structure of the recognition network unchanged,the two time-frequency graph denoising networks designed in this paper can effectively remove the noise components,retain the signal features,and improve the recognition effect under low signal-to-noise ratio.In summary,this paper effectively combines signal processing and deep learning methods to improve the recognition effect under low signal noise from two aspects:improving the signal time-frequency diagram and design,and optimizing the CNN network structure.The experimental results show that the proposed method can significantly improve the recognition accuracy under low signal-to-noise ratio.