Prof. Geoffrey Ye Li (Imperial College London)

It has been demonstrated recently that deep learning (DL) has great potential to break the bottleneck of 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 DLbased communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, modeldrive DL for signal detection. For those without block structures, we provide our recent endeavors in developing endtoend 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 the 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 problemsolving 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 mixedinteger nonlinear programming (MINLP). We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with applications in vehicular networks.

Bio:
Dr. Geoffrey Ye Li is the Chair Professor in Wireless Systems in Department of EEE, Imperial College London. Before joining Imperial College London in 2020, he was a professor with Georgia Institute of Technology, GA, USA, for 20 years and a Principal Technical Staff Member with AT&T (Bell) Labs – Research in New Jersey, USA, for around 5 years. He is currently focusing on machine learning and statistical signal processing for wireless communications. His research topics in the past couple decades include machine learning for wireless signal detection and resource allocation, cognitive radios, crosslayer optimisation for spectrum and energyefficient wireless networks, OFDM and MIMO techniques for wireless systems, and blind signal processing.

Dr. Geoffrey Ye Li was awarded IEEE Fellow for his contributions to signal processing for wireless communications in 2005. He won several prestigious awards from IEEE Signal Processing Society (Donald G. Fink Overview Paper Award in 2017), IEEE Vehicular Technology Society (James Evans Avant Garde Award in 2013 and Jack Neubauer Memorial Award in 2014), and IEEE Communications Society (Stephen O. Rice Prize Paper Award in 2013, Award for Advances in Communication in 2017, and Edwin Howard Armstrong Achievement Award in 2019). He also received the 2015 Distinguished ECE Faculty Achievement Award from Georgia Tech. He has been recognised as the HighlyCited Researcher by Thomson Reuters almost every year.



Wireless ML Seminars is a series of lectures focused on Machine Learning in the wireless space. Invited speakers for the series are leaders in their fields, hailing from respected research institutions worldwide. The seminar series is curated by Prof. Jeff Andrews and Prof. Hyeji Kim from the Wireless Networking and Communications Group at the University of Texas at Austin.

Find out more here: https://sites.utexas.edu/wirelessmlse...