Dear TCCN fellow members,
With feedback and comments from our TCCN meeting early this month, we are starting to organise free virtual seminars to our Technical Community on Cognitive Networks (TCCN) members. Please see the following talk on “Deep Learning in Wireless Communications” by Professor Geoffrey Ye Li.
Time and date: EDT 9:00am-10:00am (BST 14:00-15:00), Friday, 24th July 2020
Venue: online, registration via https://www.eventbrite.co.uk/e/free-virtual-seminar-deep-learning-in-wireless-communications-tickets-112946706640
Title: Deep Learning in Wireless Communications
Presenter: Geoffrey Ye Li, School of ECE, Georgia Tech
It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of the conventional communication systems. In this talk, we present our recent work in DL in wireless communications, including physical layer processing and resource allocation. DL can improve the performance of each individual (traditional) block in a conventional communication system or jointly 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 joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN).
Judicious resource (spectrum, power, etc.) allocation can significantly improve efficiency of wireless networks. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. Deep learning represents a promising alternative due to its remarkable power to leverage data for problem solving and can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first present our research results in using deep learning to reduce the complexity of mixed integer non-linear programming (MINLP). We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.
Dr. Geoffrey Li is currently 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 statistical signal processing and machine learning for wireless communications. In these areas, he has published over 500 referred journal and conference papers in addition to over 40 granted patents. His publications have been cited over 41,000 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 since 2001. 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, 2017 IEEE SPS Donald G. Fink Overview Paper Award, and 2019 IEEE ComSoc Edwin Howard Armstrong Achievement Award. He also won the 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech
Chair, Technical Committee on Cognitive Networks (TCCN)