基于深度学习的MPSK信号调制识别  

Modulation recognition of MPSK signals based on deep learning

作　　者：刘明骞[1,2] 郑诗斐 李兵兵[1,2] LIU Mingqian;ZHENG Shifei;LI Bingbing(State Key Laboratory of Integrated Services Networks,Xidian University,Xi′an 710071,China;Collaborative Innovation Center of Information Sensing and Understanding,Xidian University,Xi′an 710071,China)

机构地区：[1]西安电子科技大学综合业务网理论及关键技术国家重点实验室,陕西西安710071 [2]西安电子科技大学信息感知技术协同创新中心,陕西西安710071

出　　处：《国防科技大学学报》2019年第5期153-158,共6页Journal of National University of Defense Technology

基　　金：国家自然科学基金资助项目(61501348,61271299);中央高校基本科研业务费专项资金资助项目(JB180106);中国博士后科学基金资助项目(2017M611912);江苏省博士后科研资助计划(1701059B);陕西省自然科学基础研究计划资助项目(2016JQ6039);高等学校学科创新引智计划资助项目(B08038)

摘　　要：为了有效实现信号调制方式的智能识别,提出基于深度学习的多进制相移键控(Multiple Phase Shift Keying,MPSK)信号调制识别方法。分析接收MPSK信号的循环谱,并通过提取MPSK信号循环谱的等高图获得二维特征信息,利用深度学习中的卷积神经网络对二维特征进行训练,使用测试样本对所设计的调制识别方法的有效性进行验证。仿真结果表明,所提方法具有良好的识别性能。In order to realize the intelligent identification of signals modulation effectively,a novel modulation recognition method of MPSK(multiple phase shift keying)signals based on deep learning was proposed.The cycle spectrum of the MPSK signals were analyzed firstly,and the two-dimensional features information were obtained by extracting the contour map of the MPSK signals cyclic spectrum.Then,the two-dimensional features were trained by using the convolution neural network of deep learning.Finally,the effectiveness of the proposed modulation recognition method was verified by the test samples.The simulation results show that the proposed method has good recognition performance.

关 键 词：调制识别 循环谱 深度学习 卷积神经网络 

分 类 号：T[一般工业技术]