Prof. Inkyu Lee

IEEE Fellow

Korea University

The National Academy of Engineering of Korea member

BK21 Center for Humanware Information Technology

Korea

Speech title： AI-assisted air interface: Decentralized approach

Biography

Inkyu Lee (Fellow, IEEE) received the B.S. degree (Hons.) in control and instrumentation engineering from Seoul National University, Seoul, South Korea, in 1990, and the M.S. and Ph.D. degrees in electrical engineering from Stanford University, Stanford, CA, USA, in 1992 and 1995, respectively. From 1995 to 2002, he was a member of the Technical Staff with Bell Laboratories, Lucent Technologies, Murray Hill, NJ, USA, where he studied high-speed wireless system designs. Since 2002, he has been with Korea University, Seoul, where he is currently a Professor with the School of Electrical Engineering, where he has also served as the Department Head of the School of Electrical Engineering from 2019 to 2021. In 2009, he was a Visiting Professor at the University of Southern California, Los Angeles, CA. He has authored or coauthored more than 190 journal articles in IEEE publications and holds 30 U.S. patents granted or pending. His research interests include digital communications, signal processing, and coding techniques applied for next-generation wireless systems. He has been elected as a member of the National Academy of Engineering of Korea in 2015 and is currently a Distinguished Lecturer of IEEE. He was a recipient of the IT Young Engineer Award at the IEEE/IEEK Joint Award in 2006, the Best Paper Award at the Asia Pacific Conference on Communications in 2006, the IEEE Vehicular Technology Conference in 2009, the IEEE International Symposium on Intelligent Signal Processing and Communication Systems in 2013, the Best Research Award from the Korean Institute of Communications and Information Sciences in 2011, the Best Young Engineer Award from the National Academy of Engineering of Korea in 2013, and the Korea Engineering Award from the National Research Foundation of Korea in 2017. He has served as an Associate Editor for the IEEE TRANSACTIONS ON COMMUNICATIONS from 2001 to 2011 and the IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS from 2007 to 2011. In addition, he was the Chief Guest Editor for the Special Issue on 4G Wireless Systems of the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS in 2006. He currently serves as the Co-Editor-in-Chief of the Journal of Communications and Networks.

Abstract:

In this talk, I will present a new multi-agent reinforcement learning framework which can be applied to 6G systems. Limited computing resources of internet-of-things (IoT) nodes incur prohibitive latency in processing input data. This triggers new research opportunities toward task offloading systems where edge servers handle intensive computations of IoT devices. Deploying the computing servers at existing base stations may not be sufficient to support IoT nodes operating in a harsh environment. This requests mobile edge servers to be mounted on unmanned aerial vehicles (UAVs) that provide on- demand mobile edge computing (MEC) services. Time-varying offloading demands and mobility of UAVs need a joint design of the optimization variables for all time instances. Therefore, an online decision mechanism is essential for UAV-aided MEC networks. This article presents an overview of recent deep reinforcement learning (DRL) approaches where decisions about UAVs and IoT nodes are taken in an online manner. Specifically, joint optimization over task offloading, resource allocation, and UAV mobility is addressed from the DRL perspective. For the decentralized implementation, a multi-agent DRL method is proposed where multiple intelligent UAVs cooperatively determine their computations and communication policies without central coordination. Numerical results demonstrate that the proposed decentralized learning strategy is superior to existing DRL solutions. The proposed framework sheds light on the viability of the decentralized DRL techniques in designing self-organizing IoT networks