典型文献
Automatic modulation classification based on AlexNet with data augmentation
文献摘要：
Deep learning(DL)requires massive volume of data to train the network.Insufficient training data will cause serious overfitting problem and degrade the classification accuracy.In order to solve this problem,a method for automatic modulation classification(AMC)using AlexNet with data augmentation was proposed.Three data augmentation methods is considered,i.e.,random erasing,CutMix,and rotation.Firstly,modulated signals are converted into constellation representations.And all constellation representations are divided into training dataset and test dataset.Then,training dataset are augmented by three methods.Secondly,the optimal value of execution probability for random erasing and CutMix are determined.Simulation results show that both of them perform optimally when execution probability is 0.5.Thirdly,the performance of three data augmentation methods are evaluated.Simulation results demonstrate that all augmentation methods can improve the classification accuracy.Rotation improves the classification accuracy by 13.04％when signal noise ratio(SNR)is 2 dB.Among three methods,rotation outperforms random erasing and CutMix when SNR is greater than-6 dB.Finally,compared with other classification algorithms,random erasing,CutMix,and rotation used in this paper achieved the performance significantly improved.It is worth mentioning that the classification accuracy can reach 90.5％with SNR at 10 dB.
文献关键词：
中图分类号：
作者姓名：
Zhang Chengchang;Xu Yu;Yang Jianpeng;Li Xiaomeng
作者机构：
School of Optoelectric Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
文献出处：
引用格式：
[1]Zhang Chengchang;Xu Yu;Yang Jianpeng;Li Xiaomeng-.Automatic modulation classification based on AlexNet with data augmentation)[J].中国邮电高校学报（英文版）,2022(05):51-61
A类：
B类：
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AB值：
0.493692
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