摘要
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.
Abstract
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.
维普
万方数据