CFCS:A Robust and Efficient Collaboration Framework for Automatic Modulation Recognition

作     者：Jian Shi Xiaohui Yang Jia Ma Guangxue Yue Jian Shi;Xiaohui Yang;Jia Ma;Guangxue Yue

作者机构：Key Laboratory of Medical Electronics and Digital Health of Zhejiang ProvinceJiaxing 314001China School of Computer Engineering and ScienceShanghai UniversityShanghai 200444China Research Center of Cyber Science and TechnologyHangzhou Innovation InstituteBeihang UniversityHangzhou 311228China College of Information Science and EngineeringJiaxing UniversityJiaxing 314001China 

出 版 物：《Journal of Communications and Information Networks》 (通信与信息网络学报(英文))

年 卷 期：2023年第8卷第3期

页      面：283-294页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：This work was supported by the National Science Foundation of China under Grant U19B2015. 

主　　题：AMR CNN LSTM combination scheme transfer learning 

摘      要：Most of the existing automatic modulation recognition(AMR)studies focus on optimizing the network structure to improve performance,without fully considering cooperation among the basic networks to play their respective advantages.In this paper,we propose a robust and efficient collaboration framework based on the combination scheme(CFCS).This scheme effectively explores the spatial and temporal characteristics of complex signals by associating the advantages of convolutional neural network(CNN)and long and short-term memory(LSTM)network.In addition,the robustness of the CFCS is verified by transfer learning.Experiments demonstrate that the recognition rate of CFCS for highorder modulation signals such as 64QAM,128QAM,and 256QAM is more than 90%at high signal-to-noise ratios(SNRs),and 24 modulation types are effectively identified.Moreover,CFCS was transferred from RML2018.01a to RML2016.10b using transfer learning,which can still be deployed efficiently while reducing the training time by 20%.The CFCS has strong generalization ability and excellent recognition performance.