Current and Future Trends in 5G/B5G/6G

Montreal, Quebec Canada

IEEE Young Professionals Affinity Groups of the Montreal Section, Ottawa Section, Toronto Section, Turkey Section, and the IEEE Vancouver Joint Communications Chapter bring bright minds from the flagship research groups across the globe to give the community technical lectures on cutting-edge areas in wireless communications. This event will cover broad arrays of topics along with fundamental research results targeting a variety of applications in 5G/B5G/6G. Day & Time: Monday, December 14, 2020 11:45 a.m. – 3:15 p.m. Speakers: Walid Saad, Mehdi Bennis, Halim Yanikomeroglu, Wei Yu, Vincent Wong Organizers: IEEE Montreal Section YP, Vancouver Jt Chpt VT06/COM19/PHO36/BT02/IT12/ITS38, Ottawa Section YP, Turkey Section YP, Toronto Section YP Location: Virtual Contact & Register: Please visit https://events.vtools.ieee.org/m/241334 for contact information and to register. Agenda: 11:45 PM – 12:00 PM Opening and Welcome Mansour Naslcheraghi, Chair of IEEE YP Montreal 12:00 PM – 12:30 PM Talk Walid Saad 12:30 PM – 12:35 PM Q&A Walid Saad 12:35 PM – 01:05 PM Talk Halim Yanikomeroglu 01:05 PM – 01:10 PM Q&A Halim Yanikomeroglu 01:10 PM – 01:40 PM Talk Vincent Wong 01:40 PM – 01:45 PM Q&A Vincent Wong 01:45 PM – 02:15 PM Talk Mehdi Bennis 02:15 PM – 02:20 PM Q&A Mehdi Bennis 02:20 PM – 02:50 PM Talk Wei Yu 02:50 PM – 02:55 PM Q&A Wei Yu 02:55 PM – 3:10 PM Q&A All speakers Topics and Abstracts: Walid Saad, ECE Department, Virginia Tech, Blacksburg, USA Professor, Fellow of IEEE Title: Can Terahertz Communications Provide High-Rate Highly Reliable Low Latency Communications in 6G Networks? Abstract: Communication at high-frequency terahertz (THz) bands is seen as a staple of the sixth generation (6G) of wireless cellular networks, due to the large amount of available bandwidth. However, 6G systems will have to support, not only high data rates, but also highly reliable communication links for emerging applications such as advanced wireless virtual reality (VR) systems. In particular, advanced wireless VR applications will impose new visual and haptic requirements that are directly linked to the quality-of-experience (QoE) of VR users. These QoE requirements can only be met by wireless 6G connectivity that offers high-rate and high-reliability low latency communications (HRLLC), unlike the low rates usually considered in vanilla 5G ultra-reliable low latency communication scenarios.  Guaranteeing HRLLC in THz-enabled 6G systems requires dealing with the uncertainty that is specific to the THz channel. Therefore, in this talk, after a brief overview on our vision of 6G systems, we will explore the potential of THz for meeting HRLLC requirements. In this regard, we first quantify the risk for an unreliable VR performance through a novel and rigorous characterization of the tail of the end-to-end (E2E) delay. Then, we perform a thorough analysis of the tail-value-at-risk (TVaR)  to concretely characterize the behavior of extreme wireless events crucial to the real-time VR experience. We use this analysis to derive system reliability for scenarios with guaranteed line-of-sight (LoS)  as a function of THz network parameters. We then present simulation results that show how abundant bandwidth and low molecular absorption are necessary to improve the reliability, although their effect remains secondary compared to the availability of LoS, which significantly affects the THz HRLLC performance. We conclude our talk with an overview on other key open problems in the realms of THz communications and 6G systems. Halim Yanikomeroglu, ECE Department, Carleton University, Ottawa, ON, Canada Professor, Fellow of IEEE, Fellow of Canadian Academy of Engineering, Fellow of Engineering Institute of Canada Title: Wireless Access Architecture: The Next 20+ Years Abstract: Communication at high-frequency terahertz (THz) bands is seen as a staple of the sixth generation (6G) of wireless cellular networks, due to the large amount of available bandwidth. However, 6G systems will have to support, not only high data rates, but also highly reliable communication links for emerging applications such as advanced wireless virtual reality (VR) systems. In particular, advanced wireless VR applications will impose new visual and haptic requirements that are directly linked to the quality-of-experience (QoE) of VR users. These QoE requirements can only be met by wireless 6G connectivity that offers high-rate and high-reliability low latency communications (HRLLC), unlike the low rates usually considered in vanilla 5G ultra-reliable low latency communication scenarios.  Guaranteeing HRLLC in THz-enabled 6G systems requires dealing with the uncertainty that is specific to the THz channel. Therefore, in this talk, after a brief overview on our vision of 6G systems, we will explore the potential of THz for meeting HRLLC requirements. In this regard, we first quantify the risk for an unreliable VR performance through a novel and rigorous characterization of the tail of the end-to-end (E2E) delay. Then, we perform a thorough analysis of the tail-value-at-risk (TVaR)  to concretely characterize the behavior of extreme wireless events crucial to the real-time VR experience. We use this analysis to derive system reliability for scenarios with guaranteed line-of-sight (LoS)  as a function of THz network parameters. We then present simulation results that show how abundant bandwidth and low molecular absorption are necessary to improve the reliability, although their effect remains secondary compared to the availability of LoS, which significantly affects the THz HRLLC performance. We conclude our talk with an overview on other key open problems in the realms of THz communications and 6G systems. Vincent Wong, ECE Department, University of British Columbia, Vancouver, BC, Canada Professor, Fellow of IEEE Title: Throughput Optimization for Grant-Free Multiple Access with Multiagent Deep Reinforcement Learning Abstract: Grant-free multiple access (GFMA) is a promising paradigm to efficiently support uplink access of Internet of Things (IoT) devices. In this talk, we present a deep reinforcement learning (DRL)-based pilot sequence selection scheme for GFMA systems to mitigate potential pilot sequence collisions. We formulate a pilot sequence selection problem for aggregate throughput maximization in GFMA systems with specific throughput constraints as a Markov decision process (MDP). By exploiting multiagent DRL, we train deep neural networks (DNNs) to learn near-optimal pilot sequence selection policies from the transition history of the underlying MDP without requiring information exchange between the users. While the training process takes advantage of global information, we leverage the technique of factorization to ensure that the policies learned by the DNNs can be executed in a distributed manner. Simulation results show that the proposed scheme can achieve an average aggregate throughput that is close to the optimum, and has a better performance than several heuristic algorithms. Mehdi Bennis, ECE Department, University of Oulu, Finland Professor, IEEE Fellow Abstract: This talk will break down the vision of wireless network edge intelligence at scale in terms of theoretical and algorithmic principles in addition to a number of applications in beyond 5G/6G. Wei Yu, ECE Department, University of Toronto, Toronto, ON, Canada Fellow of IEEE and a Fellow of Canadian Academy of Engineering Title: Data-Driven Approaches to Wireless Communication System Design Abstract: In this talk, I will illustrate how machine learning can significantly improve the design of wireless communication systems. I will draw examples from scheduling and power control problems for wireless cellular networks to show that a data-driven approach can circumvent the need for accurate channel estimation and provide near optimal solutions to system-level optimization problems in wireless system design. I will also show how deep neural network (DNN) can be used for efficient and distributed channel estimation, quantization, feedback, and multiuser precoding for massive MIMO systems, thereby providing an efficient solution to a distributed source coding problem. I will conclude by showing the benefit of data-driven design in term of robustness. Biographies: Walid Saad of Virginia Tech Walid Saad 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 major IEEE Transactions. Halim Yanikomeroglu Dr. Halim Yanikomeroglu is a Professor in the Department of Systems and Computer Engineering at Carleton University, Canada. His extensive collaboration with industry on 4G & 5G wireless technologies resulted in 37 granted patents. During 2012-2016, he led one of the largest academic-industrial collaborative research programs on pre-standards 5G wireless. In Summer 2019, he started a new large-scale project on the 6G wireless network architecture. He supervised 26 PhD students (all completed with theses). He is a Fellow of IEEE, EIC (Engineering Institute of Canada), and CAE (Canadian Academy of Engineering), and a Distinguished Speaker for both IEEE Communications Society and IEEE Vehicular Technology Society. He served as the General Chair and Technical Program Chair of several major IEEE conferences; he also served in the Editorial Boards of several IEEE periodicals. He served as the Chair of IEEE Technical Committee on Personal Communications, and he is currently chairing the Steering Committee of IEEE’s flagship Wireless Communications and Networking Conference (WCNC). Dr. Yanikomeroglu received several awards for his research, teaching, and service, including the IEEE Wireless Communications Technical Committee Recognition Award in 2018 and the IEEE Vehicular Technology Society Stuart Meyer Memorial Award in 2020. Vincent Wong Vincent Wong is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia, Vancouver, Canada. His research areas include protocol design, optimization, and resource management of communication networks, with applications to the Internet, wireless networks, smart grid, fog computing, and Internet of Things. Currently, he is an executive editorial committee member of the IEEE Transactions on Wireless Communications, an Area Editor of the IEEE Transactions on Communications and IEEE Open Journal of the Communications Society, and an Associate Editor of the IEEE Transactions on Mobile Computing. Dr. Wong is a Fellow of the IEEE and an IEEE Communications Society Distinguished Lecturer (2019 – 2020). Mehdi Bennis Dr Mehdi Bennis is an Associate Professor at the Centre for Wireless Communications, University of Oulu, Finland, Academy of Finland Research Fellow and head of the intelligent connectivity and networks/systems group (ICON). His main research interests are in radio resource management, heterogeneous networks, game theory and distributed machine learning in 5G networks and beyond. He has published more than 200 research papers in international conferences, journals and book chapters. He has been the recipient of several prestigious awards including the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, the 2016 Best Tutorial Prize from the IEEE Communications Society, the 2017 EURASIP Best paper Award for the Journal of Wireless Communications and Networks, the all-University of Oulu award for research and the 2019 IEEE ComSoc Radio Communications Committee Early Achievement Award. Dr Bennis is an editor of IEEE TCOM and Specialty Chief Editor for Data Science for Communications in the Frontiers in Communications and Networks journal. Wei Yu Wei Yu received the B.A.Sc. degree in Computer Engineering and Mathematics from the University of Waterloo, Waterloo, Ontario, Canada in 1997 and M.S. and Ph.D. degrees in Electrical Engineering from Stanford University, Stanford, CA, in 1998 and 2002, respectively. Since 2002, he has been with the Electrical and Computer Engineering Department at the University of Toronto, Toronto, Ontario, Canada, where he is now Professor and holds a Canada Research Chair (Tier 1) in Information Theory and Wireless Communications. Prof. Wei Yu is a Fellow of the Canadian Academy of Engineering, and a member of the College of New Scholars, Artists and Scientists of the Royal Society of Canada. Prof. Wei Yu was an IEEE Communications Society Distinguished Lecturer in 2015-16. He received the Steacie Memorial Fellowship in 2015, the IEEE Marconi Prize Paper Award in Wireless Communications in 2019, the IEEE Communications Society Award for Advances in Communication in 2019, the IEEE Signal Processing Society Best Paper Award in 2017 and 2008, the Journal of Communications and Networks Best Paper Award in 2017, and the IEEE Communications Society Best Tutorial Paper Award in 2015. Professor Wei Yu currently serves as Vice President of the IEEE Information Theory Society (ITSoc) and is the President-elect of the ITSoc for 2021.

Hands-on Reinforcement Learning Workshop using Python

Montreal, Quebec Canada

IEEE Young Professionals Affinity Group Montreal brings you a free hands-on reinforcement learning workshop using Python in Google Colab. This event is co-hosted by IEEE YP Ottawa, YP Toronto, YP Vancouver, IEEE SBs of Polytechnique Montreal, Concordia, ETS, INRS, WIE Ottawa, SIGHT Montreal, and CAS technical chapter of vancouver section. All students at all levels are welcome to attend, however, registration is mandatory through the secure IEEE web portal. This workshop will cover the basics of using Colab, an introduction to reinforcement learning, and together we will write your first Q-learning code. The workshop will be interactive, and you will have a chance to code with us and ask your questions. We will also have breaks, a discussion forum, polls, and Q&A. Virtual platform info has been delivered to registrants in rounds of emails. For immediate assistance, please write us at yp.ieee.mtl@gmail.com Speakers: Sadia Khaf Sadia Khaf received the B.E. degree in electrical engineering from the National University of Sciences and Technology, School of Electrical Engineering and Computer Science (NUST-SEECS), Islamabad, Pakistan, in 2015. She received the M.Sc. degree in electrical and electronics engineering from Bilkent University, Ankara, Turkey, in 2018. From 2015 to 2018, she was a Research Assistant with IONOLAB, Turkey. From 2018 to 2020, she worked with the Faculty of Electrical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (GIKI), Pakistan, as a Lecturer. She conducted her research there on Mobile Edge Computing and Deep Learning with the TeleCoN research group. Currently, she is with École de Technologie supérieure, Montreal, Canada, as a Ph.D. student. Her research interests include reinforcement learning, radio resource management, cognitive radio networks, and industrial internet-of-things (IIoT). She was the recipient of the highest level of merit scholarships at NUST, Bilkent, and ÉTS. She also secured the P.E.O. International Peace Scholarship. She is the co-founder of SAYA school, Pakistan, and IEEE Women in Engineering (WIE) branch at ÉTS. She serves as the Vice-Chair of the IEEE ÉTS and Industrial Relations Manager of the IEEE Montreal Young Professionals Affinity Group. Faye Satari Faye Satari was born in Quchan, a small town with minimal educational infrastructure and facilities. When she finished primary school, she was accepted in the provincial entrance exam of the Exceptional Talents High School. After excelling in high school and hard working around the clock, she participated in a very competitive tuition-free nationwide university entrance exam (i.e. Konkour) among about one and half million participants; She was accepted in Computer Software Engineering of Urmia University. During her undergraduate education, she actively participated in many teamwork projects and attended some technical seminars as well as joining associations at her university. Furthermore, she got the title of top student in technical faculty of the university in one semester and received her B. Sc. degree in Computer Software Engineering from Urmia University of Technology, Urmia, Iran, in 2008. She is currently pursuing an M.Sc.A. Computer Engineering in the Department of Computer and Software Engineering, Polytechnique Montréal, University of Montreal, Montreal, Canada and she is a member of IEEE Young Professionals. Her current research interests include the Internet of Things (IoT), Smart Cities, and telecommunications systems.

The Role of AI in 5G/6G and IoT-Enabled Smart Grids

Montreal, Quebec Canada

IEEE Young Professionals brings speakers from Canadian research groups to give the community technical talks in AI-Powered Wireless Communications and Intelligent Cyber Physical Analysis in IoT-Enabled Smart Grids. The virtual platform information will be sent to registrants a couple of hours ahead of starting the event. Contact: IEEE Young Professionals Montreal Speakers: Dr. Melike Erol-Kantarci of University of Ottawa Topic: AI-Enabled Wireless Networks: A Bridge from 5G to 6G Abstract: Future wireless networks are expected to support a multitude of services demanded by Enhanced Mobile Broadband (eMBB), Ultra-Reliable and Low-latency Communications (uRLLC), and massive Machine Type Communications (mMTC) users. Heterogeneous devices with different quality of service (QoS) demands will require intelligent and flexible allocation of network resources in response to network dynamics. For instance, a highly reliable and low-latency network is needed to enable rapid transfer of messages between connected autonomous vehicles. At the same time, the same physical infrastructure is expected to serve users with high-quality video demand or even mobile Augmented/Virtual Reality entertainment applications. Next-generation wireless networks are expected to accommodate such diverse use cases. In addition, resource efficiency, reliability, and robustness are becoming more stringent for 5G and beyond networks. To meet this, future wireless networks must incorporate a paradigm shift in network resource optimization, in which efficient and intelligent resource management techniques are employed. Artificial intelligence, or more specifically machine learning algorithms stand as promising tools to intelligently manage the networks such that network efficiency, reliability, robustness goals are achieved and quality of service demands are satisfied. The opportunities that arise from learning the environment parameters under varying behavior of the wireless channel, positions AI-enabled 5G and 6G, superior to preceding generations of wireless networks. In this keynote, we will provide an overview of the state-of-art in machine learning algorithms and their applications to wireless networks, in addition to their challenges and the open issues in terms of their applicability to various functions of future wireless networks. Biography: 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 5500 times and she has an h-index of 38. 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 as 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 Hadis Karimipour Topic: Intelligent Cyber Security Analysis in IoT-Enabled Smart Grids Abstract: Today’s smart grids are complex Cyber-physical Systems (CPSs) that integrate computational and physical capabilities for controlling and managing the ever-growing number of cyber-connected devices. Aside from a fault in the physical domain, CPS also suffer from cyber-attacks in both cyber and physical domain e.g., an industrial controller can be manipulated to launch various attacks such as the device state inference attack, leading to system instability. Therefore, any effort to secure the emerging critical CPSs is of paramount importance.  Nowadays, a cyber-security specialist must detect, analyze, and defend against many cyber threats in almost real-time conditions. Without the employment of artificial intelligence and machine learning techniques, dealing with a huge number of attacks in a timely manner is not possible. Intelligent, big-data analytical techniques are necessary to mine, interpret and extract knowledge of data when there is a significant amount collected from or generated by different security monitoring solutions. This talk will go through the CPS security challenges and AI-enabled state of the art solution in the literature. Biography: Dr. Hadis Karimipour is the director of the Smart Cyber-physical System (SCPS) Lab and an Assistant Professor in the School of Engineering at the University of Guelph. She is among the pioneers of using Machine Learning (ML) for security analysis of critical infrastructure. She has published more than 80 journal articles, conference papers and book chapters in top IEEE journals and conferences. She has been a keynote/invited speaker for more than 20 different IEEE/International Conference. She was the chair of the IEEE workshop on AI for Securing Cyber-Physical System (AI4SCPS) at IEEE CCECE 2019 and IEEE CyberSciTech 2020 conferences and chair of the special session on the AI for Security of IoT-Enabled Critical Infrastructures at the IEEE SMC 2020 conference. She was the technical committee member of numerous IEEE conferences, including IEEE SEGE 2018, 2019, 2020, IEEE DSAA 2020, PST 2020, IEEE EPEC 2018, 2020, and IEEE SMC 2020. Dr. Karimipour is the Associate Editor of the Frontiers in Communications and Networks Journal, Editor of American Journal of Electrical and Electronic Engineering, and Editor of Journal of Electrical Engineering. She has also served as Guest Editor for Elsevier Journal of Computer and Electrical Engineering. She was the Editor of the Springer book on Security of Cyber-physical System. Dr. Karimipour is a Senior Member of IEEE member, chair of IEEE Women in Engineering and chapter chair of the IEEE Information Theory Kitchener-Waterloo Section, and an active member of Society for Canadian Women in Science and Technology.

Reconfigurable Intelligent Surfaces: A Signal Processing Perspective

Montreal, Quebec Canada

Wireless connectivity is becoming as essential as electricity in our modern world. Although we would like to deliver wireless broadband services everywhere, the underlying physics makes it inherently complicated: the signal power vanishes very quickly with the propagation distance and is absorbed or scattered when interacting with objects in the way. Even when we have a “strong" signal, only one in a million parts of the signal energy is being received, thus, there is a huge room for improvements! What if we could tune the propagation environment to our needs? This is the main goal of reconfigurable intelligent surfaces, which is an emerging concept for beyond-5G communications. The idea is to support the transmission from a source to a destination by deploying so-called metasurfaces that can reconfigure how incident signal waves are scattered. These surfaces can be electronically configured to interact with the wireless signals as if they had different shapes. For example, it can be configured to behave as a parabolic reflector that is rotated to gather signal energy and re-radiates it as a beam focused on the receiver. This feature makes use of a new design dimension: we can not only optimize the transmitter and receiver but also control the channel. This might be a game-changer when communicating at mmWave and THz frequencies, where the traditional propagation conditions are particularly cumbersome. This might sound like science fiction but is theoretically possible. In this talk, Dr. Emil will explain the fundamentals of this new technology from a signal processing perspective. By deriving a signals-and-systems description, we can look beyond the initial hype and understand what is actually happening when using reconfigurable intelligent surfaces. Dr. Emil will also describe recent experimental validations of the fundamentals. The talk will culminate in a description of the main research challenges that need to be tackled in the coming years. The virtual platform information will be sent to registrants a couple of hours ahead of starting the event. Contact: IEEE Young Professionals Montreal Speaker: Emil Björnson Biography: Emil Björnson received the M.S. degree in engineering mathematics from Lund University, Sweden, in 2007, and the Ph.D. degree in telecommunications from the KTH Royal Institute of Technology, Sweden, in 2011. From 2012 to 2014, he held a joint post-doctoral position at the Alcatel-Lucent Chair on Flexible Radio, SUPELEC, France, and the KTH Royal Institute of Technology. He joined Linköping University, Sweden, in 2014, where he is currently an Associate Professor. In September 2020, he became a part-time Visiting Full Professor at the KTH Royal Institute of Technology. He has authored the textbooks Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013), Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency (2017), and Foundations of User-Centric Cell-Free Massive MIMO (2021). He is dedicated to reproducible research and has made a large amount of simulation code publicly available. He performs research on MIMO communications, radio resource allocation, machine learning for communications, and energy efficiency. He has been on the Editorial Board of the IEEE Transactions on Communications since 2017. He has been a member of the Online Editorial Team of the IEEE Transactions on Wireless Communications since 2020. He has been an Area Editor in IEEE Signal Processing Magazine since 2021. He has performed MIMO research for over 14 years, his papers have received more than 12000 citations, and he has filed more than twenty patent applications. He is a host of the podcast Wireless Future and has a popular YouTube channel. He has received the 2014 Outstanding Young Researcher Award from IEEE ComSoc EMEA, the 2015 Ingvar Carlsson Award, the 2016 Best Ph.D. Award from EURASIP, the 2018 IEEE Marconi Prize Paper Award in Wireless Communications, the 2019 EURASIP Early Career Award, the 2019 IEEE Communications Society Fred W. Ellersick Prize, the 2019 IEEE Signal Processing Magazine Best Column Award, the 2020 Pierre-Simon Laplace Early Career Technical Achievement Award, and the 2020 CTTC Early Achievement Award. He also co-authored papers that received Best Paper Awards at the conferences, including WCSP 2009, the IEEE CAMSAP 2011, the IEEE SAM 2014, the IEEE WCNC 2014, the IEEE ICC 2015, and WCSP 2017.

AI against COVID-19: Screening X-ray Images for COVID-19 Infections

Virtual

Join the virtual competition on AI for COVID diagnosis, thanks to Microsoft Canada, the exclusive technology and cloud platform sponsor! The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has generated an unprecedented global health crisis, with more than 2.7 million deaths worldwide. Do you want to contribute to the fight against this pandemic? IEEE SIGHT (Special Interest Group on Humanitarian Technology) of Montreal Section, Vision and Image Processing Research Group of the University of Waterloo and DarwinAI Corp. invite data scientists, students and professionals working on Artificial Intelligence (AI) to participate in a virtual competition to help medical researchers diagnose COVID-19 with chest X-ray (CXR) images. The ultimate goal is to contribute to the development of highly accurate yet practical AI solutions for detecting COVID-19 cases and, hopefully, accelerating the treatment of those who need it the most. Moreover, this AI for Good initiative will also allow us to take action on at least one of the United Nations Sustainable Development Goals (SDGs), Good Health and Well-being. In the First Phase of the competition, the challenge consists of designing robust machine learning algorithms to predict if the subjects of study are either COVID-19 positive or COVID-19 negative. The dataset for this competition is the dataset curated by COVID-Net, a global open-source initiative launched by DarwinAI Corp., Canada, and Vision and Image Processing Research Group, University of Waterloo, Canada, for accelerating advancements in machine learning to aid healthcare workers around the world in the fight against the COVID-19 pandemic. More about the COVID-Net initiative and available open-source resources are available here. In the Second Phase, the 10 top teams of the first phase will have the opportunity to refine their solution and submit a proposal for a follow-up project to positively impact society or the academic community. This competition is organized in collaboration with the National Research Council Canada and is co-hosted by the IEEE Young Professionals Affinity Groups of Montreal, Ottawa, Toronto and Vancouver Sections, Vancouver Circuit and Systems (CAS) Technical Chapter, the Student Branches of INRS (Institut National de la Recherche Scientifique), University of Toronto and Vancouver Simon Fraser University, WIE (Women In Engineering) Ottawa. It is largely sponsored by Microsoft, and partially by the IEEE Canada Humanitarian Initiatives Committee and the IEEE Montreal Section. How to participate Note: This competition only accepts participants living in Canada, due to restrictions on funds transfer. NO PURCHASE NECESSARY TO ENTER OR WIN. The competition is hosted on the Eval.ai online platform. To participate, you or your team will need to perform the following steps: Register individually at the link provided below in the current webpage (vTools). Register yourself or your team at the link on Eval.ai: https://eval.ai/web/challenges/challenge-page/925/participate. Follow the instructions here: https://evalai.readthedocs.io/en/latest/participate.html#. Download the dataset from https://www.kaggle.com/andyczhao/covidx-cxr2. Design an AI algorithm that gets CXR images as inputs and predicts the labels of the images in the output (COVID or non-COVID). Train your AI algorithm using the training dataset. Submit your AI algorithm through Eval.ai for evaluation against the test dataset for the competition. Prizes For the First Phase, the first five best solutions will be awarded monetary prizes and Azure credits: First place: 1,000 CAD + 500 CAD in Azure. Second place: 800 CAD + 300 CAD in Azure. Third place: 600 CAD + 300 CAD in Azure. Fourth place: 400 CAD + 300 CAD in Azure. Fifth place: 300 CAD + 300 CAD in Azure. The top 10 teams on the leaderboard will also have the following opportunities: Participate in the 2nd phase to refine their solution and receive funding for a project. Write a scientific paper with the Vision and Image Processing Research Group, from the University of Waterloo, to explain their approach. For the Second Phase, the best three projects can receive funds up to the following amounts: Project 1: 5,000 CAD. Project 2: 5,000 CAD. Project 3: 4,000 CAD. Term of funding: Up to 4 months following the announcement of the selected teams. The deadline is December 31st, 2021. For more information, visit IEEE SIGHT Montreal website.  

Introduction to Python Programming

Virtual

This is an introduction to Python programming for students without any prior programming knowledge or experience. The proposed 5-day course covers the fundamental aspects of programming, which include data types, various operators, input/output, conditions, control flow, functions, and algorithms. The learning experience is enhanced by a number of examples and problem sets (data, strings, file processing and simple graphics) that will be solved interactively during the lecture with the participation of the students. The course format includes 3 hours of daily lectures. Course Objective: Attendees will gain a solid understanding of principles of programing using Python; they can progress to more advanced programming topics and explores algorithms that are integral parts of more sophisticated methodologies, e.g., Artificial Intelligence. Attendees will have the knowledge to write various Python programs, and to design algorithms manipulating files and different types of data including numbers, and text. Note: This course is designed to be offered online, and it requires the attendees to use their personal computers/laptops. Details to Join in will be forwarded to Registered Attendees Who should attend: Students, second career trainees, engineers, scientists, clinicians, and in general specialists in variety of non-STEM fields. What will you receive after completion: A certificate of completion will be given to the students who successfully complete the course and pass a short exam. Electronic copies of the course materials. Attendees will also be provided with career, and skills development advice. Speaker Dr. Alireza Sadeghian Dr. Alireza Sadeghian has been with the Department of Computer Science at Ryerson University since 1999, where he holds the position of the Professor. He is also an Affiliate Scientist at the Li Ka Shing Knowledge Institute, St. Michael's Hospital, and serves as the AI research Theme Lead in Healthcare and Analytics at the Institute for Biomedical Engineering, Science, and Technology. Dr. Sadeghian was the Chair of the Department of Computer Science from 2005 to 2015. He is the founding Director of the Advanced Artificial Intelligence Initiative (AI2) Laboratory and has extensive expertise in the areas of AI, machine learning, and Deep Learning particularly related to industrial and medical applications. He has supervised 9 postdoctoral fellows, 8 PhD, and 24 Master’s students, as well as 60 research assistants. He has published over 150 journal manuscripts, refereed conference papers, and book chapters, as well as two edited books. He has 2 invention disclosures and 2 patents. Dr. Sadeghian has been actively involved with a number of international professional and academic boards including IEEE Education Activity Board. Presently, he is the Chair of IEEE Computational Intelligence Technical Society Chapter, Toronto Section. Dr. Sadeghian is also on the Editorial Board of Applied Soft Computing Journal and serves as an Associate Editor of IEEE Access, Information Sciences, and Expert Systems Journal. Email: dr.alireza.sadeghian@ieee.org Agenda Day 1 – June 7, 2021, 6:00-9:00 pm: Introduction to computer systems, hardware architecture, CPU, memory, compilation, high level vs. low-level programming language, data representation, Python and PyCharm interactive IDE installation, writing/editing/saving/retrieving and running a simple program, basic data types, variables, assignments, comments, and expressions. The material learned will be reinforced through examples provided during the lecture. Day 2 – June 8, 2021, 6:00-9:00 pm: The following topics will be discussed: conditions, operators (arithmetic, logic, and comparison), control statements (if and if-else), and loops (for and while). The material learned will be reinforced through examples provided during the lecture. Day 3 – June 9, 2021, 6:00-9:00 pm: Students will be introduced to Strings and text files in Python. They will learn how to work with files, reading/writing text and numbers from/to a file, string manipulation, indexing, and string slicing. The material learned will be reinforced through examples provided during the lecture. Day 4 – June 10, 2021, 6:00-9:00 pm: Functions, arguments, and return values will be discussed. The material learned will be reinforced through examples provided during the lecture. Day 5 – June 11, 2021, 6:00-9:00 pm: The topics of lists and dictionaries will be discussed. Students will learn about the basic operators, creating, accessing, slicing, adding, removing, replacing, and iteration methods for lists and dictionaries. The material learned will be reinforced through examples provided during the lecture.

Rate-Splitting Multiple Access for 6G

Virtual

Virtual platform will be delivered to registrants a couple of hours before starting the event.  Contact: IEEE Montreal Young Professionals Abstract: Rate Splitting Multiple Access (RSMA), based on (linearly or nonlinearly) precoded Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers, has emerged as a novel, general and powerful framework for the design and optimization of non-orthogonal transmission, multiple access, and interference management strategies in future MIMO wireless networks. RSMA relies on the split of messages and the non-orthogonal transmission of common messages decoded by multiple users, and private messages decoded by their corresponding users. This enables RSMA to softly bridge and therefore reconcile the two extreme strategies of fully decode interference and treat interference as noise. RSMA has been shown to generalize, and subsume as special cases, four seemingly different strategies, namely Space Division Multiple Access (SDMA) based on linear precoding (currently used in 5G), Orthogonal Multiple Access (OMA), Non-Orthogonal Multiple Access (NOMA) based on linearly precoded superposition coding with SIC, and physical-layer multicasting. RSMA boils down to those strategies in some specific conditions, but outperforms them all in general. Through information and communication theoretic analysis, RSMA is shown to be optimal (from a Degrees-of-Freedom region perspective) in a number of scenarios and provides significant room for spectral efficiency, energy efficiency, fairness, reliability, QoS enhancements in a wide range of network loads and user deployments, robustness against imperfect Channel State Information at the Transmitter (CSIT), as well as feedback overhead and complexity reduction over conventional strategies used in 5G. The benefits of RSMA have been demonstrated in a wide range of scenarios (MU-MIMO, massive MIMO, multi-cell MIMO/CoMP, overloaded systems, NOMA, multigroup multicasting, mmwave communications, communications in the presence of RF impairments and superimposed unicast and multicast transmission, relay,…) and systems (terrestrial, cellular, satellite, …). Thanks to its versatility, RSMA has the potential to tackle challenges of modern communication systems and is a gold mine of research problems for academia and industry, spanning fundamental limits, optimization, PHY and MAC layers, and standardization. This lecture will share key principles of RSMA, recent developments, emerging applications and opportunities of RSMA for 6G networks and will cover many of the topics currently investigated as part of the new IEEE special interest group on RSMA https://sites.google.com/view/ieee-comsoc-wtc-sig-rsma/home. Speaker(s): Bruno Clerckx Biography: Bruno Clerckx is a (Full) Professor, the Head of the Wireless Communications and Signal Processing Lab, and the Deputy Head of the Communications and Signal Processing Group, within the Electrical and Electronic Engineering Department, Imperial College London, London, U.K. He received the M.S. and Ph.D. degrees in applied science from the Université Catholique de Louvain, Louvain-la-Neuve, Belgium, in 2000 and 2005, respectively. From 2006 to 2011, he was with Samsung Electronics, Suwon, South Korea, where he actively contributed to 4G (3GPP LTE/LTE-A and IEEE 802.16m) and acted as the Rapporteur for the 3GPP Coordinated Multi-Point (CoMP) Study Item. Since 2011, he has been with Imperial College London, first as a Lecturer from 2011 to 2015, Senior Lecturer from 2015 to 2017, Reader from 2017 to 2020, and now as a Full Professor. From 2014 to 2016, he also was an Associate Professor with Korea University, Seoul, South Korea. He also held various long or short-term visiting research appointments at Stanford University, EURECOM, National University of Singapore, The University of Hong Kong, Princeton University, The University of Edinburgh, The University of New South Wales, and Tsinghua University.

Integrated Access and Backhaul for 5G and Beyond

Virtual - Zoom

Note: Virtual platform will be delivered to registrants a couple of hours before starting the event. Contact: IEEE Montreal Section Young Professionals Abstract: The number of devices requesting for wireless communications is growing exponentially. Network densification via the deployment of many base stations (BSs) of different types is one of the mechanisms that can be employed to satisfy the ever-increasing demand for bandwidth/capacity in wireless networks. However, deploying fiber to the small cells may be expensive and impractical when the number of small cells increases. For this reason, as well as because of the traffic jams and infrastructure displacements caused by fiber optic installation, millimeter wave (mmw)-based wireless backhaul is currently considered as an alternative, providing (almost) the same rate as fiber optic with significantly less price and no digging. With this background, integrated access and backhaul (IAB) networks, where the operator can utilize part of the radio resources for wireless backhauling, has recently received considerable attention. The purpose of IAB is to replace existing backhaul systems with flexible wireless backhaul using the existing 3GPP bands providing not only backhaul but also existing cellular services in the same node. This creates more flexibility and reduces the implementation cost. For 5G NR, IAB is currently considered as a work item in 3GPP, and it is known as one of the main novelties of 5G. In this talk, we review the main backhauling techniques, and present the main motivations/standardization agreements on IAB. Moreover, We present comparisons between the IAB networks and the cases where all or part of the small access points are fiber-connected. Finally, we study the robustness of IAB networks to environmental effects and verify the effect of the blockage, the tree foliage, the rain as well as the antenna height/gain on the coverage rate of IAB setups, as the key differences between the fiber-connected and IAB networks. As we show, IAB is an attractive setup enabling 5G and beyond.

Terahertz Days: The role of Directional Terahertz Communications in the 6G era: Usage scenarios, system concepts, promises and challenges

Montreal, Quebec, Canada, Virtual: https://events.vtools.ieee.org/m/285255

The exploitation of the THz band is expected to catalyze 6G applications, as a solution to both wireless backhaul and fronthaul, and integrated backhaul/fronthaul applications. However, the utilization of THz wireless technologies comes with several challenges, mainly associated with the very high propagation losses in the THz regime, which require the utilization of high-gain directional antennas with strict beam alignment requirements, and with challenging blockage scenarios, which call for intelligent beam steering and blockage avoidance based medium access and resource allocation. In this talk, we first discuss critical usage scenarios and significant technology pillars defining the THz Wireless system concept. Then, a quantitative assessment of intelligent pencil beamforming wireless access technologies is presented, along with the impact of beam misalignment and blockage. Finally, the benefits of reconfigurable intelligent surfaces are reported and their expected role in future THz wireless systems is discussed. Speaker(s): Angeliki Alexiou, Gunes Karabulut Kurt Agenda: 11:55 AM - 12:00: Workshop opening by Mansour Naslcheraghi, Chair of YP Montreal 12:00 AM - 12:05: Terahertz Days workshop series introduction and speaker introduction by chair of Terahertz Days, Dr. Gunes Karabulut Kurt 12:05 AM - 12:50: Talk by Prof. Angeliki Alexiou 12:50 AM - 01:00 or more (depending on speaker's availability): Q & A session Montreal, Quebec, Canada, Virtual: https://events.vtools.ieee.org/m/285255

3GPP Standards for 5G New Radio: from Release 15 and beyond

Montreal, Quebec, Canada

Event to introduce the 3GPP standardization process and discuss the existing and future specifications for 5G New Radio. The fifth and latest generation of cellular mobile communication protocols (5G) is meant to address use cases well beyond the next decade. The first set of technical specifications for 5G, also referred to as “New Radio” or NR in 3GPP, were completed as part of 3GPP Release 15. The standardization work for 5G NR continues and new features are continuously added to address more advanced use cases and verticals. This presentation will provide an overview of the standardization process in 3GPP and an overview of the technical features for Release 16 and Release 17 of the specifications. The presentation will conclude with an outlook of future wireless evolution. Speaker(s): Benoît Pelletier, Montreal, Quebec, Canada

MATLAB Deep learning Seminar

Virtual: https://events.vtools.ieee.org/m/314598

Join MathWorks and IEEE to learn Deep-Learning Seminar. Dr. Aycan Hacioglu (MathWorks) will demonstrate how to manage, automated labelling and augment large data sets. We will also show you how to leverage pre-trained models such as GoogLeNet, ResNet for transfer learning and more! Co-sponsored by: Vancouver Section Affinity Group,YP Virtual: https://events.vtools.ieee.org/m/314598

Starlink use cases – particularly rural inclusion, pop-up LTE, ships and critical comms

Virtual: https://events.vtools.ieee.org/m/360280

Starlink orbits at about 550km, which results in lower latency, in comparison to GEO satellites which orbit around 35,786km. Each satellite features a compact, flat-panel design that minimizes volume, allowing for a dense launch stack to take full advantage of the launch capabilities of SpaceX's Falcon 9 rocket. join us to learn more about Starlink Speaker(s): Tim Belfall Virtual: https://events.vtools.ieee.org/m/360280