【講演概要】Deep learning has been widely applied to automatic modulation classification (AMC), but existing solutions need to train different models independently for each SNR, which leads to undeniable overhead. I will introduce a unified generative adversarial network for IQ constellations of various SNRs, requiring a single model training and introducing multi-conditions embedding and multi-domain classification to leverage both modulation type and SNR. Later, I will briefly introduce the Korean Institute of Information Scientists and Engineers (KIISE), founded in 1973. 2023 is the 50th anniversary year of KIISE (1973~2023).
【略歴】Wonjun Lee received B.S. and M.S. degrees in computer engineering from Seoul National University, Seoul, Republic of Korea, in 1989 and 1991, respectively, and the Ph.D. degree in computer science and engineering from the University of Minnesota, Minneapolis, MN, USA, in 1999. In 2002, he joined the Computer Science and Engineering Department faculty at Korea University. He is a Fellow of the IEEE, KAST (Korean Academy of Science and Technology), and NAEK (The National Academy of Engineering of Korea).