Deep Dive into the White Paper on Machine Learning in 6G Wireless Communication Networks
Wednesday 14 October 2020 at 5:00-6:30 PM (Helsinki, UTC/GMT +03:00, CET +01:00)
The 6G Research Visions Webinar Series: Machine Learning in 6G Wireless Communication Networks highlights the key results of the expert group that prepared the 6G White Paper on Machine Learning in Wireless Communications. In this white paper, we have provided and overview of the role of machine learning in 6G wireless communication networks. Applications of machine learning in physical layer, MAC layer, and application layer is discussed. Implementation and standaridization aspects are also presented.
The webinar is moderated by Dr. Samad Ali from University of Oulu who led the Expert Group.
Expert Group representatives, presenting some of the group’s key discoveries, include Dr Samad Ali, University of Oulu and Nokia Bell Labs, Oulu, Finland, Prof. Walid Saad, Virginia Tech, Blacksburg, USA, Prof. Nandana Rajatheva, University of Oulu, Oulu, Finland and Daniel Steinbach, InterDigital, New York, USA.
After the talks, a minimum of 30 minutes is reserved for discussion on machine learning in 6G wireless communication networks. A recorded video of the webinar will be openly available after the event.
Samad Ali received the B.S. degree in electrical engineering from the University of Tabriz, Iran, and the M.S. and Ph.D. degrees in wireless communications engineering from the University of Oulu, Finland. He is currently a Senior Research Specialist at Nokia Bell Labs, Oulu, Finland and 6G AI Team Lead at the 6G Flagship project at the Center for Wireless Communications, University of Oulu. His research interests include machine learning in wireless communication, machine type communications and URLLC.
Walid Saad (S’07, M’10, SM’15, F’19) received his Ph.D degree from the University of Oslo in 2010. He is currently a Professor at the Department of Electrical and Computer Engineering at Virginia Tech, where he leads the Network sciEnce, Wireless, and Security (NEWS) laboratory. His research interests include wireless networks, machine learning, game theory, security, unmanned aerial vehicles, cyber-physical systems, and network science. Dr. Saad is a Fellow of the IEEE and an IEEE Distinguished Lecturer. He is also the recipient of the NSF CAREER award in 2013, the AFOSR summer faculty fellowship in 2014, and the Young Investigator Award from the Office of Naval Research (ONR) in 2015. He was the author/co-author of nine conference best paper awards at WiOpt in 2009, ICIMP in 2010, IEEE WCNC in 2012, IEEE PIMRC in 2015, IEEE SmartGridComm in 2015, EuCNC in 2017, IEEE GLOBECOM in 2018, IFIP NTMS in 2019, and IEEE ICC in 2020. He is the recipient of the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, of the 2017 IEEE ComSoc Best Young Professional in Academia award, of the 2018 IEEE ComSoc Radio Communications Committee Early Achievement Award, and of the 2019 IEEE ComSoc Communication Theory Technical Committee. He was also a co-author of the 2019 IEEE Communications Society Young Author Best Paper. From 2015-2017, Dr. Saad was named the Stephen O. Lane Junior Faculty Fellow at Virginia Tech and, in 2017, he was named College of Engineering Faculty Fellow. He received the Dean’s award for Research Excellence from Virginia Tech in 2019. He currently serves as an editor for the IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, and IEEE Transactions on Cognitive Communications and Networking. He is an Editor-at-Large for the IEEE Transactions on Communications.
Nandana Rajatheva is currently a Professor with the Centre for Wireless Communications (CWC),University of Oulu, Finland. He is a Senior Member, IEEE and received the B.Sc. (Hons.) degree in electronics and telecommunication engineering from the University of Moratuwa, Sri Lanka, in 1987,ranking first in the graduating class, and the M.Sc. and Ph.D. degrees from the University of Manitoba, Winnipeg, MB, Canada, in 1991 and 1995, respectively. He was a Canadian Commonwealth Scholar during the graduate studies in Manitoba. He held Professor/Associate Professor positions at the University of Moratuwa and the Asian Institute of Technology (AIT), Thailand, from 1995 to 2010. He
has co-authored more than 200 refereed papers published in journals and in conference proceedings. His research interests include waveforms, channel coding for URLLC, applications of beyond 5G, machine learning in PHY/MAC, and autonomous vehicles.
Dan Steinbach BSEE Cornell University, MSEE Syracuse University, MBA Hofstra University
After work early in his career on sonar, radar and wired communications systems, Dan has been working on wireless technology for over 20 years.
Dan has worked on defining and implementing systems from WCDMA 3G TDD and FDD technology through current 5G standards. In addition he has worked on Gigabit 802.11 based systems operating at 70, 60 and 28 GHz bands.
He has been a key contributor to phased array development and demonstration platforms used to highlight InterDigitalís technology.
Most recently, he has been looking at Machine Learning solutions in the wireless physical layer.
We provide an overview of the role of machine learning in 6G wireless communication networks. By looking at various problems in different layers of the communications protocol stack, we provide a suitable machine learning tool for each problem.
Applications that will be able to utilize machine learning within the broader scope of wireless communications such as UAV communications and networking are also studied and novel ideas and future directions for them are provided.