首页|低信噪比下基于深度学习TCNN-MobileNet的调制识别

低信噪比下基于深度学习TCNN-MobileNet的调制识别

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  • 国家科技期刊平台
  • NETL
  • NSTL
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将深度学习算法应用于调制识别任务是近年来通信领域的一个研究热点，但现有方法存在网络复杂度高、硬件要求高、在低信噪比(SNR)下识别准确率不高等问题。结合离散小波变换方法，提出一种基于双路卷积神经网络级联可分离卷积网络(TCNN-MobileNet)的调制识别方法。首先通过小波变换对数据进行预处理，将信号作为输入传送到双路卷积神经网络中进行不同维度的特征提取;然后通过融合层进行特征融合并送入轻量级神经网络MobileNetV1中，进行调制识别模型训练;最后通过全连接层进行11种调制识别的分类输出。在公开数据集RML2016。10a上的实验结果表明，在-20 dB的低SNR下TCNN-MobileNet的识别准确率可达88。71%，在18 dB的高SNR下识别准确率可达96。66%，SNR在-20～18 dB范围内时平均识别准确率为88。37%，相比于ResNet18、ResNet34等经典网络架构提升了约35%。TCNN-MobileNet识别方法在保证识别精度不变的情况下能够降低训练参数量以及网络训练时间，有效简化网络架构，降低对硬件设备的要求，对轻量级神经网络在调制识别中的应用具有借鉴意义。
Deep Learning TCNN-MobileNet-Based Modulation Recognition Under Low Signal-to-Noise Radio
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.

modulation recognitionConvolutional Neural Network(CNN)Wavelet Transform(WT)deep learninglow Signal-to-Noise Ratio(SNR)

牛瑞婷、严天峰、高锐、王映植

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兰州交通大学电子与信息工程学院,甘肃 兰州 730070

兰州交通大学甘肃省无线电监测及定位行业技术中心,甘肃 兰州 730070

兰州交通大学甘肃省高精度北斗定位技术工程实验室,甘肃 兰州 730070

调制识别 卷积神经网络 小波变换 深度学习 低信噪比

国家自然科学基金甘肃省重点人才项目甘肃省青年科技基金四电BIM工程与智能应用铁路行业重点实验室2022年度开放课题

62161017666001020121JR7RA325BIMKF-2022-03

2024

10.19678/j.issn.1000-3428.0068243

计算机工程
华东计算技术研究所　上海市计算机学会

计算机工程

CSTPCD北大核心
影响因子：0.581
ISSN：1000-3428
年,卷(期)：2024.50(7)