TY - EJOU AU - Tu, Ya AU - Lin, Yun AU - Wang, Jin AU - Kim, Jeong-Uk TI - Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification T2 - Computers, Materials \& Continua PY - 2018 VL - 55 IS - 2 SN - 1546-2226 AB - 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. KW - Deep Learning KW - automated modulation classification KW - semi-supervised learning KW - generative adversarial networks DO - 10.3970/cmc.2018.01755