IEEE Training School On Machine Learning For Wireless Communications

What is Machine Learning

Scientific study of algorithms and statistical models that computer systems use to perform a specific task without using overt directions, but depending on the patterns and the inference instead is known as Machine Learning (ML). It is often referred to as a subgroup of artificial intelligence. A mathematical model is built by the ML algorithm, known as “training data”. This model is used to make predictions or decisions without being explicitly programmed to perform the task. Wide variety of applications such as email filtering and computer vision use ML algorithms, where it is problematic or infeasible to develop a conventional algorithm for effectively performing the task. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.

Machine Learning in Wireless Communications

Machine learning and data driven approaches have recently received much attention as a key enabler for future 5G and beyond wireless networks. Yet, the evolution towards learning-based data driven networks is still in its infancy, and much of the realization of the promised benefits requires thorough research and development. Fundamental questions remain as to where and how ML can really complement the well-established, well-tested communication systems designed over the last 4 decades. Moreover, adaptation of machine learning methods is likely needed to realize their full potential in the wireless context. This is particularly challenging for the lower layers of the protocol stack, where the constraints, problem formulation, and even the objectives may fundamentally differ from the typical scenarios to which machine learning has been successfully applied in recent years. In addition, a deep understanding of the fundamental performance limits is also essential in order to establish quality-of-service guarantees that are common in communication system design. All such research challenges lie at the core of this special issue

Information about speakers

Prof. John S Thompson is currently Professor of Signal Processing and Communications at the Institute for Digital Communications, in the School of Engineering. His main research interests are in: Millimetre Wave Wireless Communications; Signal Processing for Wireless Networks; Smart Grid Concepts for Energy Efficiency; Green Communications Systems and Networks Rapid Prototyping of MIMO Detection Algorithms, including the Fixed Sphere Decoder; From 2014-17, he coordinated the Marie Curie International Training Network ADVANTAGE. He has been listed by Thomson Reuters from 2015-18 as a Highly Cited Scientist. He is a co-author of the second edition of book entitled "Digital Signal Processing: Concepts and Applications".

Dr. Marco Di Renzo is working as a CNRS Research Director - CentraleSupelec, Paris-Saclay University. His main research interests are in the area of wireless communications theory, signal processing, and information theory. Dr. Di Renzo is the recipient of the special mention for the outstanding five–year (1997–2003) academic career, University of L’Aquila, Italy; the THALES Communications fellowship for doctoral studies (2003–2006), University of L’Aquila, Italy; and the Torres Quevedo award for his research on ultra-wide band systems and cooperative localization for wireless networks (2008–2009), Ministry of Science and Innovation, Spain.

Prof. Neeraj Kumar is working as a Full Professor in the Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology (Deemed to be University), Patiala (Pb.), India. He is also adjunct professor at Asia University, Taiwan, King Abdul Aziz University, Jeddah, Saudi Arabia and Charles Darwin University, Australia. He has published more than 400 technical research papers in top-cited journals and conferences which are cited more than 16302 times from well-known researchers across the globe with current h-index of 69 (Google scholar). He is highly cited researcher in 2019 in the list released by Web of Science (WoS). His broad research areas are Green computing and Network management, IoT, Big Data Analytics, Deep learning and cyber-security. He has also edited/authored 10 books with International/National Publishers like IET, Springer, Elsevier, CRC, etc.

Prof. Dush Nalin Jayakody (IEEE Senior Member) received the Ph. D. degree in Electronics, Electrical, and Communications Engineering, from the University College Dublin, Ireland in 2014. From 2014 - 2016, he was a Postdoc Research Fellow at the Institute of computer science, University of Tartu, Estonia and Department of Informatics, University of Bergen, Norway. From 2016, he is a professor at the School of Computer Science & Robotics, National Research Tomsk Polytechnic University, Russia. Prof. Jayakody also serves as the Head of Research & the Educational Centre on Automation and Information Technologies, TPU. Since 2019, he also serves as the Head of School of Postgraduate & Research, Sri Lanka Technological Campus, Padukka 10500 Sri Lanka. Prof. Jayakody has published over 160 international peer reviewed journal and conference papers. His research interests include PHY and NET layer prospective of 5G communications technologies such as NOMA for 5G etc, Cooperative wireless communications, device to device communications, LDPC codes, Unmanned Ariel Vehicle etc.

Prof. Jayakody is a Senior Member of IEEE and he has served as workshop chair, session chair or technical program committee member for various international conferences, such as IEEE PIMRC 2013-2019, IEEE WCNC 2014-2018, IEEE VTC 2015-2018 etc. He currently serves as an Area Editor the Elsevier Physical Communications Journal, MDPI Information journal and Wiley Internet of Technology Letters. In his career, so far, he has attracted nearly $6M research funding. Also, he serves as a reviewer for various IEEE Transactions and other journals.

Dr. Melike Erol-Kantarci is Canada Research Chair in AI-enabled Next-Generation Wireless Networks and Associate Professor at the School of Electrical Engineering and Computer Science at the University of Ottawa. She is the founding director of the Networked Systems and Communications Research (NETCORE) laboratory. She is a Faculty Affiliate at the Vector Institute, Toronto, and the Institute for Science, Society and Policy at University of Ottawa. She has over 150 peer-reviewed publications which have been cited over 5700 times and she has an h-index of 39. She has received numerous awards and recognitions. Recently, she received the 2020 Distinguished Service Award of the IEEE ComSoc Technical Committee on Green Communications and Computing. She was named in N2Women Stars in Computer Networking and Communications in 2019. Dr. Erol-Kantarci has delivered 50+ keynotes, tutorials and panels around the globe and has acted as the general chair and technical program chair for many international conferences and workshops. Her main research interests are AI-enabled wireless networks, 5G and 6G wireless communications, smart grid and Internet of things. She is an IEEE ComSoc Distinguished Lecturer, IEEE Senior member and ACM Senior Member.

Dr. E.S. Gopi is a senior IEEE member with two decades of teaching and research experience. He has authored seven books and five book chapters. He has several papers in international journals and conferences to his credit. He is also the coordinator for the Pattern Recognition and Computational Intelligence Laboratory and the COMPSIG newsletter. He is the editor for the proceedings of the international conference on “Machine Learning, Deep leaning and Computational intelligence for wireless communication” (MDCWC2020). He is one of the series editor for the book series on “Signals and Communication”, Springer publications. His book on “Pattern recognition and Computational intelligence using Matlab” is being recognized as one of the best Pattern recognition book by Book authority. His research interests include pattern recognition, signal processing, and computational intelligence.

Mr. Medhat Elsayed is a PhD candidate at the University of Ottawa. He obtained his BSc and MSc degrees from Cairo University, Egypt, in 2009 and 2013 respectively. His research interests include wireless networks, 6G and beyond, machine learning, and reinforcement learning. Mr. Elsayed has received the International Doctoral Scholarship from the University of Ottawa in 2018. He also received the NSERC CREATE TOPSET graduate research training scholarship from the University of Ottawa in 2018. Mr. Elsayed delivered a tutorial at the Second Annual Workshop of the Ottawa AI Alliance held in NRC of Ottawa in 2019. He is the author of several publications addressing wireless communication problems via reinforcement learning, with an h-index of 6. In addition, he is a co-inventor of a filed patent.

Program (UTC +7)

Download program (pdf-version)

Wed, 24th March 2021 Watch online Link - https://zoom.us/j/99105210361	Thuesday, 25th March 2021 Watch online Link:https://zoom.us/j/91072628383	Friday, 26th March 2021 Watch online Link:https://zoom.us/j/91230028847
Introduction & Welcoming Session 10.00 - 10.30 Andrey Yakovlev, Rector, Tomsk Polytechnic University, Russia. D. N. K. Jayakody, General Chair of the Training School, Tomsk. Polytechnic University, Russia. Marwa Chafii, General Co-chair of the training School, ENSEA, ETIS, France.	Linear Regression techniques for wireless communication, Part 1 09.30 - 11.00 Prof. E.S. Gopi, IEEE Senior Member, Department of ECE, NIT Tiruchirappalli, India.	AI-Enabled Wireless Networks: A Bridge from 5G to 6G 09.30 - 11.00 Melike Erol-Kantarci, IEEE Senior Member, University of Ottawa, Canada
Towards Smart Radio Environment Empowered by Reconfigurable Intelligent Metasurfaces & AI 10.30 - 12.30 Marco Di Renzo, IEEE Fellow, CentraleSupélec Paris, France	Linear Regression techniques for wireless communication, Part II 11.00 - 12.30 Prof. E.S. Gopi, IEEE Senior Member, Department of ECE, NIT Tiruchirappalli, India.	5G Radio Resource Management using Deep and Transfer Reinforcement Learning 11.00 - 12.30 Medhat Elsayed, University of Ottawa, Canada Melike Erol-Kantarci, University of Ottawa, Canada
12.30-13.30 Break	12.30-13.30 Break	12.30-13.30 Break
Deep Learning for 5G and Beyond: Potential Solutions and Challenges 13.30-15.00 Neeraj Kumar, IEEE Senior Member, ThaparInstitute of Engineering and Technology, Adjunct Professor at Charles Darwin, University, Australia.	Intelligent UAV Deployment for a Disaster-Resilient Wireless Network 13.30 - 15.30 Dushantha Nalin K. Jayakody, IEEE Senior Member, Fellow, IET, Tomsk Polytechnic University, Russia.	Dimensionality reduction techniques for wireless communication Part 1 13.30 - 15.00 E.S. Gopi, IEEE Senior Member, Department of ECE, NIT Tiruchirappalli, India.
15.00 - 15.30 Break	15.30 - 16.00 Break	15.00 - 15.30 Break
Deep Learning for 5G and Beyond: Potential Solutions and Challenges 15.30 - 17.00 Neeraj Kumar, IEEE Senior Member, ThaparInstitute of Engineering and Technology, Adjunct Professor at Charles Darwin, University, Australia.	Overview of Artificial Intelligence Technologies for Future Wireless Systems 16.00-17.00 John S Thompson, IEEE Fellow, Institute for Digital Communications, University of Edinburgh, UK.	Dimensionality reduction techniques for wireless communication Part II 15.30 - 17.00 E.S. Gopi, IEEE Senior Member, Department of ECE, NIT Tiruchirappalli, India.