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Deep Learning in Physical Layer Communications Prof. Geoffrey Li, IEEE Fellow Georgia Institute of Technology, USA Abstract Recent research has demonstrated that machine learning (DL) has great potentials to break the bottleneck of communication systems. This presentation introduces our recent work in DL in physical layer communications. DL can improve the performance of each individual (traditional) module in communication systems or optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we present a couple of examples in channel estimation and signal detection. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems. At the end of the talk, we discuss some potential research directions in the area. Biography Dr. Geoffrey Li is a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include wireless communications and statistical signal processing. In these areas, he has published over 400 referred journal and conference papers in addition to over 40 granted patents. His publications have been cited by over 32,000 times and he has been listed as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters almost every year. He has been an IEEE Fellow since 2006. He received 2010 IEEE ComSoc Stephen O. Rice Prize Paper Award, 2013 IEEE VTS James Evans Avant Garde Award, 2014 IEEE VTS Jack Neubauer Memorial Award, 2017 IEEE ComSoc Award for Advances in Communication, and 2017 IEEE SPS Donald G. Fink Overview Paper Award. He also won the 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech. |
