基于特征融合的UFMC系统调制识别算法  

Modulation and recognition algorithm of UFMC system based on feature fusion

作　　者：吴云戈 张天骐[1] 李春运 吴仙越 Wu Yunge;Zhang Tianqi;Li Chunyun;Wu Xianyue(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《电子测量技术》2024年第11期51-58,共8页Electronic Measurement Technology

基　　金：重庆市自然基金(cstc2021jcyj-msxmX0836)项目资助。

摘　　要：非协作通信通用滤波多载波(UFMC)系统子载波所存在的调制识别问题有待解决,对此,提出一种基于特征融合的UFMC系统调制识别算法。首先得到UFMC系统接收端信号,并提取同相正交序列和幅度相位序列作为输入特征;接着构建神经网络模块,构建方法如下:将卷积神经网络与长短时记忆网络串联,并将门控循环单元与上述模块并联;最后,对UFMC系统子载波进行调制识别。实验结果表明,构建的神经网络能够有效识别5种信号(BPSK、4QAM、8QAM、16QAM、64QAM),并且在信噪比≥6 dB时识别的准确率达到100%。The modulation recognition problem of subcarriers in the universal filter multi-carrier(UFMC)system for non-cooperative communication needs to be addressed.Therefore,a modulation recognition algorithm based on feature fusion is proposed for the UFMC system.Firstly,the receiver signal of the UFMC system is obtained and input features such as in-phase and quadrature sequence and amplitude phase sequence are extracted.Subsequently,a neural network module is constructed by connecting a convolutional neural network with a long short-term memory network in series,while also incorporating a gated recurrent unit in parallel.Finally,modulation recognition of UFMC system subcarriers is performed.The experimental results demonstrate that the constructed neural network effectively identifies five signals(BPSK,4QAM,8QAM,16QAM,64QAM)with a recognition accuracy reaching 100%when signal to noise ratio greater than or equal to 6 dB.

关 键 词：通用滤波多载波 调制识别 同相正交序列 神经网络 

分 类 号：TN911.7[电子电信—通信与信息系统]