计算机工程2024，Vol.50Issue(7)：204-215,12.DOI:10.19678/j.issn.1000-3428.0068243
低信噪比下基于深度学习TCNN-MobileNet的调制识别
Deep Learning TCNN-MobileNet-Based Modulation Recognition Under Low Signal-to-Noise Radio
摘要
Abstract
In recent years,the application of deep learning algorithms to modulation recognition tasks has become a research hotspot in the field of communication.However,existing methods suffer from high network complexity,high hardware requirements,and low recognition accuracy under low Signal-to-Noise Ratio(SNR).A modulation recognition method based on a two-way convolutional neural network cascade separable convolutional network called TCNN-MobileNet is proposed,which combines the Discrete Wavelet Transform(DWT)method.First,the data are preprocessed using Wavelet Transform(WT),and the signal is transmitted as input to a dual convolutional neural network for feature extraction in different dimensions.Subsequently,feature fusion is performed through the fusion layer and fed into the lightweight neural network MobileNetV1 for modulation recognition model training.Finally,11 types of modulation recognition are classified and output through a fully connected layer.The experimental results on the publicly available dataset RML2016.10a show that the recognition accuracy of TCNN-MobileNet can reach 88.71%at a low SNR of-20 dB,96.66%at a high SNR of 18 dB,and an average recognition accuracy of 88.37%in the SNR range of-20 dB to 18 dB,which is approximately 35%higher than classical network architectures such as ResNet18 and ResNet34.The TCNN-MobileNet recognition method can reduce the number of training parameters and the network training time while ensuring that the recognition accuracy remains unchanged,effectively simplifying the network architecture and reducing hardware requirements.This is significant for the application of lightweight neural networks in modulation recognition.关键词
调制识别/卷积神经网络/小波变换/深度学习/低信噪比Key words
modulation recognition/Convolutional Neural Network(CNN)/Wavelet Transform(WT)/deep learning/low Signal-to-Noise Ratio(SNR)分类
信息技术与安全科学引用本文复制引用
牛瑞婷,严天峰,高锐,王映植..低信噪比下基于深度学习TCNN-MobileNet的调制识别[J].计算机工程,2024,50(7):204-215,12.基金项目
国家自然科学基金(62161017) （62161017）
甘肃省重点人才项目(6660010201) （6660010201）
甘肃省青年科技基金(21JR7RA325) （21JR7RA325）
四电BIM工程与智能应用铁路行业重点实验室2022年度开放课题(BIMKF-2022-03). （BIMKF-2022-03）
