Abstract
This paper implements a deep learning‐based modulation pattern recognition algorithm for communication signals using a convolutional neural network architecture as a modulation recognizer. In this paper, a multiple‐parallel complex convolutional neural network architecture is proposed to meet the demand of complex baseband processing of all‐digital communication signals. The architecture learns the structured features of the real and imaginary parts of the baseband signal through parallel branches and fuses them at the output according to certain rules to obtain the final output, which realizes the fitting process to the complex numerical mapping. By comparing and analyzing several commonly used time‐frequency analysis methods, a time‐frequency analysis method that can well highlight the differences between different signal modulation patterns is selected to convert the time‐frequency map into a digital image that can be processed by a deep network. In order to fully extract the spatial and temporal characteristics of the signal, the CLP algorithm of the CNN network and LSTM network in parallel is proposed. The CNN network and LSTM network are used to extract the spatial features and temporal features of the signal, respectively, and the fusion of the two features as well as the classification is performed. Finally, the optimal model and parameters are obtained through the design of the modulation recognizer based on the convolutional neural network and the performance analysis of the convolutional neural network model. The simulation experimental results show that the improved convolutional neural network can produce certain performance gains in radio signal modulation style recognition. This promotes the application of machine learning algorithms in the field of radio signal modulation pattern recognition.
Highlights
Secure and efficient transmission of information is the basic requirement of wireless communication
The results show that the improved convolutional neural network and classical convolutional neural network proposed in this paper have advantages in the accuracy of modulation pattern recognition
The problem of communication signal modulation pattern recognition based on deep learning is studied
Summary
Secure and efficient transmission of information is the basic requirement of wireless communication. Wireless Communications and Mobile Computing achieving many practical results, it has led researchers to introduce deep learning into the process of modulation pattern recognition of communication signals, hoping to make communication devices capable of self-learning and selfrenewal, so that they can better cope with the problems and challenges brought about by the complex electromagnetic environment and the increase of modulation patterns in the future [3]. The third chapter analyzes and studies the communication signal feature processing and explains the specific implementation of the algorithm, and the design study of the modulation identifier is carried out.
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