基于联合特征参数和一维CNN的MIMO-OFDM系统调制识别算法  

Modulation recognition algorithm for MIMO-OFDM system based on joint characteristic parameters and one-dimensional CNN

作　　者：汪锐 张天骐[1] 安泽亮 王雪怡 方竹 WANG Rui;ZHANG Tianqi;AN Zeliang;WANG Xueyi;FANG Zhu(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：《系统工程与电子技术》2023年第3期902-912,共11页Systems Engineering and Electronics

基　　金：国家自然科学基金(61671095,61702065,61701067,61771085);信号与信息处理重庆市市级重点实验室建设项目(CSTC2009CA2003);重庆市自然基金(cstc2021jcyj-msxmX0836);重庆市教育委员会科研项目(KJ1600427,KJ1600429)资助课题。

摘　　要：针对当前非协作通信中多输入多输出正交频分复用(multiple-input multiple-output orthogonal frequency division multiplexing,MIMO-OFDM)系统子载波的调制识别问题,提出了一种基于一维卷积神经网络(one-dimensional convolutional neural network,1D-CNN)的调制识别方法。首先,利用特征矩阵的联合近似对角化(joint approximate diagonalization of eigenvalue matrix,JADE)算法从接收端的混合信号中恢复发送信号;然后,提取恢复信号的循环谱切片和四次方谱作为浅层特征;最后,利用1D-CNN对特征进行训练,使用测试样本对所提出的调制识别方法进行仿真验证。仿真结果表明,所提方法对MIMO-OFDM系统中的5种信号可以进行有效识别,在信噪比为10 dB时的识别精度即可达到100%。Aiming at the modulation identification of subcarriers in multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)system in non cooperative communication,a modulation identification method based on one-dimensional convolutional neural network(1D-CNN)is proposed.Firstly,the joint approximate diagonalization of eigenvalue matrix(JADE)algorithm is used to recover the transmission signal from the mixed signal at the receiver.Then,the cyclic spectrum slice and quartic spectrum of the recovery signal are extracted as shallow features.Finally,the features are trained by 1D-CNN,and the proposed modulation recognition method is simulated and verified by test samples.Simulation results show that the proposed method can effectively identify five signals in MIMO-OFDM system,and the recognition accuracy can reach 100%when the signal-to-noise ratio is 10 dB.

关 键 词：多输入多输出正交频分复用 调制识别 循环谱 四次方谱 一维卷积神经网络 

分 类 号：TN911.7[电子电信—通信与信息系统]