Modulation recognition method based on multi-inputs convolution neural network

Xiong ZHA; Hua PENG; Xin QIN; Guang LI; Tianyun LI

doi:10.11959/j.issn.1000-436x.2019206

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Modulation recognition method based on multi-inputs convolution neural network

Papers | 更新时间：2024-06-05

- Modulation recognition method based on multi-inputs convolution neural network
- Journal on Communications Vol. 40, Issue 11, Pages: 30-37(2019)
- 作者机构：
  
  信息工程大学信息系统工程学院，河南郑州 450001
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61401511)
- DOI：10.11959/j.issn.1000-436x.2019206
  CLC： TN911.7
- Online First：2019-11，
  
  Published：25 November 2019
- 稿件说明：
移动端阅览
Xiong ZHA, Hua PENG, Xin QIN, et al. Modulation recognition method based on multi-inputs convolution neural network[J]. Journal on Communications, 2019, 40(11): 30-37.
DOI：

Xiong ZHA, Hua PENG, Xin QIN, et al. Modulation recognition method based on multi-inputs convolution neural network[J]. Journal on Communications, 2019, 40(11): 30-37. DOI： 10.11959/j.issn.1000-436x.2019206.

摘要

为识别当前卫星通信系统所采用的主要调制方式，提出了一种基于多端卷积神经网络的通信信号调制识别算法。利用信号的先验信息以及对网络拓扑结构的认知，将信号时域波形转化为眼图和矢量图，作为信号的浅层特征表达，并由此设计了基于多端卷积神经网络的调制识别模型。通过训练所搭建的网络，对浅层特征进行深度提取和映射，最终完成了目标信号的调制识别。仿真实验表明，所提算法相对于传统调制识别算法以及目前基于波形和星座图的深度学习识别算法识别效果更好，当信噪比为5 dB时，识别性能可达95%。

Abstract

In order to identify the main modulation modes adopted in current satellite communication systems

a signal modulation recognition algorithm based on multi-inputs convolution neural network was proposed.With the prior information of the signals and knowledge of the network topological structure

the time-domain signal waveforms were converted into eye diagrams and vector diagrams to represent the shallow features of the signals.Meanwhile

the modulation recognition model based on multi-inputs convolution neural network was designed.Through the training of the network

the shallow features were deeply extracted and mapped.Finally

the signal modulation recognition task was completed.The simulation results show that compared with the traditional algorithms and deep learning algorithms

the proposed method has a better anti-noise performance

and the overall recognition rate of this algorithm can reach 95% when the signal-to-noise ratio is 5 dB.

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references

廖灿辉 , 涂世龙 , 万坚 . 一种抗频偏的卫星幅相调制信号识别算法 [J ] . 电子与信息学报 , 2014 , 36 ( 2 ): 346 - 352 .

LIAO C H , TU S L , WAN J . An anti-frequency-offset algorithm for modulation recognition of satellite amplitude-phase modulated signals [J ] . Journal of Electronics and Information Technology , 2014 , 36 ( 2 ): 346 - 352 .

刘明骞 , 李兵兵 , 曹超凤 , 等 . 认知无线电中非高斯噪声下数字调制信号识别方法 [J ] . 通信学报 , 2014 , 35 ( 1 ): 82 - 88 .

LIU M Q , LI B B , CAO C F , et al . Recognition method of digital modulation signals over non-Gaussian noise in cognitive radio [J ] . Journal on Communications , 2014 , 35 ( 1 ): 82 - 88 .

NANDI A K , AZZOUZ E E . Algorithms for automatic modulation recognition of communication signals [J ] . IEEE Transactions on Communications , 1998 , 46 ( 4 ): 431 - 436 .

SWAMI A , SADLER B M . Hierarchical digital modulation classification using cumulants [J ] . IEEE Transactions on Communications , 2000 , 48 ( 3 ): 416 - 429 .

WEE T N , HI ASLAM M W , ZHU Z , et al . Automatic modulation classification using combination of genetic programming and KNN [J ] . IEEE Transactions on Wireless Communications , 2012 , 11 ( 8 ): 2742 - 2750 .

LI R , LI L , YANG S , et al . Robust automated VHF modulation recognition based on deep convolutional neural networks [J ] . IEEE Communications Letters , 2018 , 22 ( 5 ): 946 - 949 .

DENG L , YU D . Deep learning:methods and applications [J ] . Foundations ＆ Trends in Signal Processing , 2014 , 7 ( 3 ): 197 - 387 .

KULIN M , KAZAZ T , MOERMAN I , et al . End-to-end learning from spectrum data:a deep learning approach for wireless signal identification in spectrum monitoring applications [J ] . IEEE Access , 2018 , 6 ( 99 ): 18484 - 18501 .

PENG S L , JIANG H Y , WANG H X , et al . Modulation classification based on signal constellation diagrams and deep learning [J ] . IEEE Transactions on Neural Networks and Learning System , 2019 30 ( 3 ): 718 - 727 .

ETSI . Digital video broadcasting (DVB) user guidelines for the second generation system for broadcasting,interactive services,news gathering and other broadband satellite applications (DVB-S2) [S ] . TR 102 376 v1.1.1 . 2005 .

KRIZHEYSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks [C ] // International Conference on Neural Information Processing Systems . NIPC , 2014 : 1097 - 1105 .

IOFFE S , SZEGEDY C . Batch normalization:accelerating deep network training by reducing internal covariate shift [C ] // International Conference on International Conference on Machine Learning . JMLR , 2015 : 448 - 456 .

HE K , ZHANG X , REN S , et al . Deep residual learning for image recognition [C ] // Computer Vision and Pattern Recognition . IEEE , 2016 : 770 - 778 .

MORELLI M , ANDREA A N D , MENGALI U . Feedforward ML-based timing estimation with PSK signals [J ] . IEEE Communications Letters , 1997 , 1 ( 3 ): 80 - 82 .

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