脉冲噪声下基于域自适应的调制识别  

Modulation recognition based on domain adaptation under impulsive noises

作　　者：张晓林 李阳[1] 孙溶辰 ZHANG Xiaolin;LI Yang;SUN Rongchen(College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China)

机构地区：[1]哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001

出　　处：《哈尔滨工程大学学报》2024年第9期1840-1847,共8页Journal of Harbin Engineering University

基　　金：国家自然科学基金项目(62001139).

摘　　要：针对基于深度神经网络进行调制识别会存在模型失配导致识别率显著下降的问题,本文提出了一种基于域自适应的调制识别算法,在测试样本与训练样本存在较大分布差异时,仍具有很高的识别率。该算法对脉冲噪声下的调制信号的星座图进行处理,恢复出几何特征,强化特征并生成图片,将生成的图片输入到深度域自适应网络中完成调制识别。该算法实现了兼具大动态范围的广义信噪比和大动态范围的脉冲强度条件下的调制识别。仿真实验表明:该算法可以有效解决模型失配问题,具有良好的识别性能。As modulation recognition based on deep neural networks has a problem of model mismatch,which leads to a significant decrease in recognition rate,this paper proposes a modulation recognition algorithm based on domain adaptation.When there is a large distribution difference between test and training samples,it still has a high recog-nition rate.First,the algorithm processes the constellation diagram of modulated signals under pulse noise to recov-er geometric features.It then enhances the features and generates images,which are input into a deep domain ad-aptation network to complete modulation recognition.This algorithm can realize modulation recognition under both generalized signal-to-noise ratio with a large dynamic range and pulse intensity with a large dynamic range.Simula-tion results show that this algorithm can solve the model mismatch problem effectively,having good recognition per-formance.

关 键 词：调制识别 脉冲噪声 星座图 特征强化 深度学习 模型失配 迁移学习 域自适应 

分 类 号：TN911[电子电信—通信与信息系统]