@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}
}