Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification

作     者：Ya Tu Yun Lin Jin Wang Jeong-Uk Kim 

作者机构：College of Information and Communication EngineeringHarbin Engineering UniversityHarbin 150001China School of Computer&Communication EngineeringChangsha University of Science&TechnologyChangsha 410114China School of Information EngineeringYangzhou UniversityYangzhou 225009China Department of Energy GridSangmyung UniversitySeoulKorea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2018年第55卷第5期

页      面：243-254页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work is supported by the National Natural Science Foundation of China(Nos.61771154 61603239 61772454 6171101570). 

主　　题：Deep Learning automated modulation classification semi-supervised learning generative adversarial networks 

摘      要：Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.