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基于多尺度特征融合的调制识别算法

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针对缺失无线电信号先验信息、人工选取特征操作复杂以及低信噪比时识别率不高的问题,提出一种基于多尺度特征融合的残差收缩网络(MFRSN)调制识别算法.在包含PAM4、BPSK、QPSK、8 PSK、CPFSK、GFSK、QAM16、QAM64、WBFM、AM-SSB和AM-DSB的11种调制类型数据集上进行的仿真实验结果表明,加入软阈值分支后,低信噪比信号平均识别准确率提高2.87%,同时多尺度特征融合方法对比其他网络结构有更好的类内识别效果.
MODULATION RECOGNITION ALGORITHM BASED ON MUITI-SCALE FEATURE FUSION
Aimed at the problems of missing prior information of radio signal,complex operation of manual feature selection and low recognition rate at low SNR,a modulation recognition algorithm based on multi-scale feature residual shrinkage networks(MFRSN)is proposed.The simulation experiment was carried out on the data set containing 11 modulation types,such as PAM4,BPSK,QPSK,8PSK,QAM16,CPFSK,GFSK,QAM16,QAM64,WBFM,AM-SSB,AM-DSB.The results show that the average recognition accuracy of the signal with low SNR is improved by 2.87%after adding the soft threshold branch,at the same time,multi-scale feature fusion method has better intra class recognition effect compared with other network structures.

Modulation recognitionSelf-learning soft threshold branchMulti-scale feature fusionResidual network

朱宽、余勤

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四川大学电气工程学院 四川成都 610065

调制识别 自学习软阈值分支 多尺度特征融合 残差神经网络

四川省重点研发项目

2020YFG0051

2024

计算机应用与软件
上海市计算技术研究所 上海计算机软件技术开发中心

计算机应用与软件

CSTPCD北大核心
影响因子:0.615
ISSN:1000-386X
年,卷(期):2024.41(10)