Modulation Recognition Method of Complex Modulation Signal Based on Convolution Neural Network

机译：基于卷积神经网络的复模信号调制识别方法

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With the emergence and further integration of computer technology, software radio technology and network information technology, signal analysis and processing are inseparable from the critical moment of modulation recognition. For the mixed data of the modulation signal and the secondary modulation signal, this paper simulates the modulation recognition by combining convolutional neural networks with different architectures and signal cyclic spectrum images under the Pytorch framework through image enhancement. This paper also expands the identification of the modulation mode of the secondary modulation signal and provides the improvement direction of the modulation signal data set.

机译：随着计算机技术的出现和进一步集成，软件无线电技术和网络信息技术，信号分析和处理与调制识别的关键时刻密不可分。对于调制信号和二次调制信号的混合数据，本文通过通过图像增强将卷积神经网络与不同架构下的卷积神经网络组合来模拟调制识别。通过图像增强，Pytorch框架下的信号循环图像。本文还扩展了辅助调制信号的调制模式的识别，并提供调制信号数据集的改进方向。

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《IEEE Joint International Information Technology and Artificial Intelligence Conference》|2020年|1179-1184|共6页
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Rui Liu; Yunxin Guo; Shibing Zhu;
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Deep learning; Image recognition; Modulation; Computer architecture; Convolutional neural networks; Information technology; Image enhancement;

机译：深入学习;图像识别;调制;计算机架构;卷积神经网络;信息技术;图像增强;

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专利

1. Automatic Recognition of Communication Signal Modulation Based on the Multiple-Parallel Complex Convolutional Neural Network [J] . Zhen Huang, Chengkang Li, Qiang Lv, Wireless communications & mobile computing . 2021,第a期

机译：基于多并行复合卷积神经网络自动识别通信信号调制
2. Modulation Recognition of Radio Signals Based on Edge Computing and Convolutional Neural Network [J] . Jiyu Jiao, Xuehong Sun, Yanpeng Zhang, 通信与信息网络学报（英文） . 2021,第003期

机译：基于边缘计算和卷积神经网络的无线电信号调制识别
3. A deep learning method based on convolutional neural network for automatic modulation classification of wireless signals [J] . Xu Yu, Li Dezhi, Wang Zhenyong, Wireless Networks . 2019,第7期

机译：基于卷积神经网络的无线信号自动调制分类的深度学习方法
4. Modulation Pattern Recognition of M-QAM Signals Based on Convolutional neural network And Extreme learning machine [C] . Wanpei Chen, Shen Gao, Tao Zhang, International Symposium on Artificial Intelligence and Robotics . 2020

机译：基于卷积神经网络和极限学习机的M-QAM信号调制模式识别
5. A Human Visual System Inspired Feature Recognition Method Using Convolutional Neural Networks [D] . Koester, Evan. 2018

机译：基于卷积神经网络的人类视觉系统启发特征识别方法
6. Analysis of the pattern recognition algorithm of broadband satellite modulation signal under deformable convolutional neural networks [O] . Hui Li, Ming Li 2020

机译：可变形卷积神经网络下宽带卫星调制信号模式识别识别算法分析
7. Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network and Deep Q-Learning Network [O] . Zhiyu Qu, Chenfan Hou, Changbo Hou, 2020

机译：基于卷积神经网络和深Q学习网络的雷达信号脉冲内调制识别
8. Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks. [R] . Smith, A., Evans, M., Downey, J. 2017

机译：基于累积量和神经网络的卫星通信信号调制分类。

Modulation Recognition Method of Complex Modulation Signal Based on Convolution Neural Network

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