Prof. Kwang-Cheng Chen
IEEE Fellow
University of South Florida, USA

Kwang-Cheng Chen has been a Professor at the Department of Electrical Engineering, University of South Florida, since 2016. From 1987 to 2016, Dr. Chen worked with SSE, Communications Satellite Corp., IBM Thomas J. Watson Research Center, National Tsing Hua University, HP Labs., and National Taiwan University in mobile communications and networks. He visited TU Delft (1998), Aalborg University (2008), Sungkyunkwan University (2013), and Massachusetts Institute of Technology (2012-2013, 2015-2016). He founded a wireless IC design company in 2001, which was acquired by MediaTek Inc. in 2004. He has been actively involving in the organization of various IEEE conferences and serving editorships with a few IEEE journals, together with various IEEE volunteer services to the IEEE, Communications Society, Vehicular Technology Society, and Signal Processing Society, such as founding the Technical Committee on Social Networks in the IEEE Communications Society. Dr. Chen also has contributed essential technology to various international standards, namely IEEE 802 wireless LANs, Bluetooth, LTE and LTE-A, 5G-NR, and ITU-T FG ML5G. He has authored and co-authored over 300 IEEE publications, 4 books published by Wiley and River (most recently, Artificial Intelligence in Wireless Robotics, 2020), and more than 24 granted US patents. Dr. Chen is an IEEE Fellow and has received a number of awards including 2011 IEEE COMSOC WTC Recognition Award, 2014 IEEE Jack Neubauer Memorial Award, 2014 IEEE COMSOC AP Outstanding Paper Award. Dr. Chen’s current research interests include wireless networks, artificial intelligence and machine learning, quantum communications and computing, IoT/CPS, social networks and data analytics, and cybersecurity.

Speech Title: Wireless Networked Multi-Robot Systems in a Smart Factory
Abstract: Industry 4.0 based on artificial intelligence and information communication technology emerges as a primary contributor to the digital economy and Internet of Things. In order to execute flexible production in real-time, smart manufacturing must holistically integrate 6G wireless networking, AI computing, sensing, and automatic control technologies. This talk explores the challenges of this complex systems engineering scenario, by modeling the operation of a smart factory as a wireless networked multi-robot system of production robots and transportation robots. The real-time multi-robot task assignment for flexible production has been innovated in a computationally effective way. Furthermore, the special sequential decision-making of a multi-robot manufacturing system is examined toward a multi-robot system of high yield. Applying social learning extends the resilience of precision operation in a networked multi-robot system while taking network topology into consideration. Wireless networked multi-robot system integrating AI and wireless comtrol reveals the new landscape of smart factories and Industry 4.0.

Prof. Inkyu Lee
IEEE Fellow
Korea University, South Korea

Inkyu Lee 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. He has also served as the Department Head for the School of Electrical Engineering, Korea University, from 2019 to 2021. In 2009, he was a Visiting Professor with the University of Southern California, Los Angeles, CA, USA. 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. Dr. Lee 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 a Chief Guest Editor for the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (Special Issue on 4G Wireless Systems) in 2006. He currently serves as the Co-Editor-in-Chief for the Journal of Communications and Networks. 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, and is an IEEE Fellow.

Speech Title: Deep Learning Approaches for Multiuser Communication Systems
Abstract: In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. In this talk, I will present an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, a novel deep learning (DL) framework is proposed which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information.

At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes.

Prof. James Xiaojiang Du
IEEE Fellow
Stevens Institute of Technology, USA

Xiaojiang (James) Du is the Anson Wood Burchard Endowed-Chair Professor in the Department of Electrical and Computer Engineering at Stevens Institute of Technology. He was a professor at Temple University. Dr. Du received his B.S. from Tsinghua University, Beijing, China in 1996. He received his M.S. and Ph.D. degree in Electrical Engineering from the University of Maryland, College Park in 2002 and 2003, respectively. His research interests are security, wireless networks, and systems. He has authored over 500 journal and conference papers in these areas, as well as a book published by Springer. Dr. Du has been awarded more than 8 million US Dollars research grants from the US National Science Foundation (NSF), Army Research Office, Air Force Research Lab, the State of Pennsylvania, and Amazon. He won the best paper award at IEEE ICC 2020, IEEE GLOBECOM 2014 and the best poster runner-up award at the ACM MobiHoc 2014. He serves on the editorial boards of three IEEE journals. Dr. Du is an IEEE Fellow and a Life Member of ACM.

Speech Title: HAWatcher: Semantics-Aware Anomaly Detection for Appified Smart Homes
Abstract: As IoT devices are integrated via automation and coupled with the physical environment, anomalies in an appified smart home, whether due to attacks or device malfunctions, may lead to severe consequences. Prior works that utilize data mining techniques to detect anomalies suffer from high false alarm rates and missing many real anomalies. Our observation is that data mining-based approaches miss a large chunk of information about automation programs (also called smart apps) and devices. We propose Home Automation Watcher (HAWatcher), a semantics-aware anomaly detection system for appified smart homes. HAWatcher models a smart home’s normal behaviors based on both event logs and semantics. Given a home, HAWatcher generates hypothetical correlations according to semantic information, such as apps, device types, relations and installation locations, and verifies them with event logs. The mined correlations are refined using correlations extracted from the installed smart apps. The refined correlations are used by a Shadow Execution engine to simulate the smart home’s normal behaviors. During runtime, inconsistencies between devices’ real-world states and simulated states are reported as anomalies. We evaluate our prototype on the SmartThings platform in four real-world testbeds and test it against totally 62 different anomaly cases. The results show that HAWatcher achieves high accuracy, significantly outperforming prior approaches. This work has been accepted by one of the top four security conferences - USENIX Security 2021 (acceptance rate = 17%).