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Dr. Sastri L Kota

Title: New Satellite Frontiers for Beyond 5G and 6G Wireless Networks


Dr. Kota is President of SoHum Consultants and Adjunct Professor in University of Oulu, Finland. He has more than 40 years of systems engineering experience. Dr. Kota’s major contributions span three segments of Satellite and Space sector, including system development, International Standardization and research and teaching. He held technical and management positions at Harris, Loral Space, Lockheed Martin, SRI International, the MITRE Corp, Xerox Corp. and Computer Science Corp. He contributed to satellite and wireless communication network systems, digital video broadcasting, mobile communications, broadband internet and hybrid networks to commercial and defense programs. His entrepreneur efforts included as a chief network architect of IP satellite network for Broadband Multimedia Services (BMS) and was responsible for networking aspects of Ka- band satellite called Astrolink. He provided leadership in international standardization of broadband satellite networks as head of the U.S. delegation and the U.S. chair of the ITU-R Working group developing recommendations for FSS, MSS and BSS. He also led the wireless ATM of the ATM Forum. 

Currently, Dr. Kota, as a Working Group Co-Chair leads 5G Satellite, an IEEE Future Network Initiative. He has authored and co-authored 200 papers in conference proceedings and journals, and five books: Broadband Satellite Communications for Internet Access; Emerging Location Aware Broadband Wireless Ad Hoc Networks; Satellite TCP/IP in High Performance Networking; Trends in Broadband Networking in Wiley Encyclopedia of Telecommunications; and Modeling and Simulation Environment for Satellite and Terrestrial Communication Networks. Dr. Kota conducted a lecture series and tutorials on computer networking, broadband satellite networks, digital video broadcasting at MILCOM, IEEE, and AIAA conferences; University of Oulu Finland, LNMIT, and MNIT, Jaipur, India; University of Sienna, Italy, and University of Catalunya, Spain. He was a lecturer for five years at IIT, Roorkee, India. Dr. Kota served as a guest editor of special issues for IEEE Communications, Wireless, VTS, International Journal of Satellite Communications and Networking, Space Communications, and Wireless Information Networks. Dr. Kota was a keynote speaker at conferences and symposiums and served as an Unclassified Technical Program Chair/Executive Member of MILCOM 2007, 2004, and 1997. He also served as Symposium Co-Chair of IEEE GLOBECOM, Technical Chair of ISWPC2007, Technical committees and Workshop Chair/Invited Sessions Chair of ICC, PIMRC, and IWSSC. He was also the organizer and chair for ICSSC/AIAA conferences. He received IEEE Communications Society, Satellite and Space Communications Technical Committee (SSC) Distinguished Service Award.  He was selected as a Fulbright Specialist by the U.S. Department of State and received the Golden Quill Awards from Harris Corporation for Project Leadership and publications in the field of Broadband Satellite Communications for Internet and Assured Communications. Dr. Kota holds a Ph. D from University of Oulu, Finland, an Engineer’s Degree from Northeastern University, Boston, Massachusetts, and an MSEE from IIT, Roorkee, India. He is a Life Senior Member of IEEE and an Associate Fellow of AIAA.


Beyond 5G and 6G wireless networks provide high data rates, lower end-to-end latency, massive device connectivity, and consistent user quality of experience provisioning. The Roadmap of 5G enablers include Internet of Things (IoT) and machine to machine (M2M) services and expected to transform the way we live and work. In addition, 6G research and development will be impacted to some extent by the UN’s Sustainable Development Goals. Satellite systems are uniquely positioned to provide a solution to the future networks by integrating beyond 5G and 6G. Satellite communications will play a significant role as a complementary solution for ubiquitous coverage, broadcast provision, emergency /disaster recovery and remote and rural areas coverage. 

This talk provides an overview of the emerging opportunities, challenges and possible solutions for such integrated network architectures. The presentation includes a seamless integration of satellite into B5G as a complementary solution to the terrestrial networks, due to its ubiquitous coverage, and broadcast/multicast nature in rural and urban areas. Various satellite system design options including LEO/MEO/GEO and spectrum bands of Ka/Q/V are discussed. The new LEO mega constellation systems are described. The potential use of integrated architectures with application of Unmanned Aerial Vehicles (UAV) and High Altitude Platforms (HAPs) are briefly described.  The High Throughput Satellites (HTS) are envisaged a satellite throughput of tera bits per second, makes 5G satellite systems fully realizable. In conclusion, an overview of the current standardization efforts by IEEE Roadmap, ITU-T/R, 5GPP, 3GPPP is presented. 


Dr. Tony Q.S. Quek

Title: AI: A Networking and Communication Perspective


Tony Q.S. Quek received the B.E. and M.E. degrees in Electrical and Electronics Engineering from Tokyo Institute of Technology, Tokyo, Japan, respectively. At Massachusetts Institute of Technology (MIT), Cambridge, MA, he earned the Ph.D. in Electrical Engineering and Computer Science. Currently, he is the Cheng Tsang Man Chair Professor with Singapore University of Technology and Design (SUTD). He also serves as the Head of ISTD Pillar, Sector Lead for SUTD AI Program, and the Deputy Director of SUTD-ZJU IDEA. His current research topics include wireless communications and networking, big data processing, network intelligence, URLLC, and IoT. 

Dr. Quek has been actively involved in organizing and chairing sessions and has served as a TPC member in numerous international conferences. He is currently serving as an Editor for the IEEE Transactions on Wireless Communications, the Chair of IEEE VTS Technical Committee on Deep Learning for Wireless Communications as well as an elected member of the IEEE Signal Processing Society SPCOM Technical Committee. He was an Executive Editorial Committee Member of the IEEE Transactions on Wireless Communications, an Editor of the IEEE Transactions on Communications, and an Editor of the IEEE Wireless Communications Letters. He is a co-author of a few books published by Cambridge University Press.

Dr. Quek received the 2008 Philip Yeo Prize for Outstanding Achievement in Research, the 2012 IEEE William R. Bennett Prize, the 2016 IEEE Signal Processing Society Young Author Best Paper Award, the 2017 CTTC Early Achievement Award, the 2017 IEEE ComSoc AP Outstanding Paper Award, the 2020 IEEE Communications Society Young Author Best Paper Award, the 2020 IEEE Stephen O. Rice Prize, and the 2016-2019 Clarivate Analytics Highly Cited Researcher. He is a Distinguished Lecturer of the IEEE Communications Society and a Fellow of IEEE. 


Recent breakthroughs in artificial intelligence and machine learning, including deep neural networks, the availability of powerful computing platforms and big data are providing us with technologies to perform tasks that once seemed impossible. In 6G, autonomous vehicles and drones, intelligent mobile networks, and intelligent internet-of-things (IoT) will become a norm. At the heart of this technological revolution, it is clear that we will need to have artificial intelligence over a massively scalable, ultra-high capacity, ultra-low latency, and dynamic new network infrastructure. In this talk, we will provide an overview of AI from the perspective of networking and communications. In addition, we will also share some of our preliminary works in this area. 


Dr. Geoffrey Ye Li

Title: Deep Learning in Wireless Communications


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 42,000 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. 


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.