电讯技术2023，Vol.63Issue(12)：1869-1875,7.DOI:10.20079/j.issn.1001-893x.230829006
通道门控Res2Net卷积神经网络自动调制识别
Channel Gated Res2Net Convolutional Neural Network for Automatic Modulation Recognition
陈昊 1郭文普 1康凯1
作者信息
- 1. 火箭军工程大学 作战保障学院,西安 710025
- 折叠
摘要
Abstract
In response to the problem of low accuracy in automatic modulation recognition under low signal-to-noise ratio(SNR)conditions,the authors propose a channel gated residual 2-network(Res2Net)convolutional neural network(CNN)model.The model mainly consists of two-dimensional CNN(2D-CNN),multi-scale Res2Net,squeeze-and-excitation network(SENet)and long short-term memory(LSTM)network,which extracts multi-scale features from raw I/Q data through convolution,adjusts the weight of feature channels through gating mechanism,and uses LSTM to model the sequence of convolutional features to ensure effective data feature mining,thereby improving the accuracy of automatic modulation recognition.The modulation recognition experiment on the benchmark dataset RML2016.10a shows that the recognition accuracy of the proposed model is 92.68%at 12 dB SNR,and the average recognition accuracy is above 91% when the SNR is greater than 2 dB.Compared with classical CLDNN model,LSTM model,similar PET-CGDNN model and CGDNet model,the proposed model can achieve higher modulation type recognition accuracy.关键词
自动调制识别/卷积神经网络/压缩与激励网络/多尺度残差网络/长短期记忆网络Key words
automatic modulation recognition/convolutional neural network/squeeze-and-excitation network/multi-scale residual network/long short-term memory network分类
信息技术与安全科学引用本文复制引用
陈昊,郭文普,康凯..通道门控Res2Net卷积神经网络自动调制识别[J].电讯技术,2023,63(12):1869-1875,7.
