@Article{cmc.2018.01755, AUTHOR = {Ya Tu, Yun Lin, Jin Wang, Jeong-Uk Kim}, TITLE = {Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification}, JOURNAL = {Computers, Materials \& Continua}, VOLUME = {55}, YEAR = {2018}, NUMBER = {2}, PAGES = {243--254}, URL = {http://www.techscience.com/cmc/v55n2/22895}, ISSN = {1546-2226}, ABSTRACT = {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 Pro-cessing. Since Automated Modulation Classification (AMC) is an important part in Cognitive Radio Networks, we try to explore its potential in solving signal modula-tion recognition problem. It cannot be overlooked that DL model is a complex mod-el, 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 unla-beled data effectively to reduce over-fitting in DL. In this paper, we extend Genera-tive Adversarial Networks (GANs) to the semi-supervised learning will show it is a method can be used to create a more data-efficient classifier.}, DOI = {10.3970/cmc.2018.01755} }