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.

中文翻译：

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基于多并行复卷积神经网络的通信信号调制自动识别

本文使用卷积神经网络架构作为调制识别器，实现了一种基于深度学习的通信信号调制模式识别算法。本文提出了一种多并行复卷积神经网络架构，以满足全数字通信信号复杂基带处理的需求。该架构通过并行分支学习基带信号实部和虚部的结构化特征，并在输出端按照一定的规则融合得到最终输出，实现了对复杂数值映射的拟合过程。通过比较分析几种常用的时频分析方法，选择能够很好地突出不同信号调制模式差异的时频分析方法，将时频图转换为可以被深度网络处理的数字图像。为了充分提取信号的时空特征，提出了CNN网络和LSTM网络并行的CLP算法。CNN网络和LSTM网络分别用于提取信号的空间特征和时间特征，并进行两种特征的融合以及分类。最后，通过基于卷积神经网络的调制识别器的设计和卷积神经网络模型的性能分析，得到最优模型和参数。仿真实验结果表明，改进后的卷积神经网络在无线电信号调制方式识别方面可以产生一定的性能提升。这促进了机器学习算法在无线电信号调制模式识别领域的应用。