Archives
IEEE Technical Seminar by Melike Erol-Kantarci
IEEE VDL Talk by Feifei Gao
Date and time: May 18, 2021, from 5 pm to 6:30 pm
IEEE VDL Talk by Shiwen Mao
IEEE Virtual Distinguished Lecturer (VDL) Talk, co-organized by the IEEE New York Section Chapter, North New Jersey Section Joint Chapter, IEEE Vancouver Joint Communications Chapter, and other chapters.
Date and time: Sunday, May 2, 2021, at 5 pm (PDT)
IEEE Technical Seminar by Ibrahim Gedeon of TELUS
IEEE Technical Seminar, co-organized by the IEEE Seattle Joint ComSoc/VT/BT/IT/ITS Chapter and IEEE Vancouver Joint Communications Chapter
Date and time: Thursday, April 22, 2021, at 6 pm
IEEE Technical Seminar (Dr. Charlie Jackson)
IEEE Technical Seminar, co-organized by the IEEE Seattle Joint ComSoc/VT/BT/IT/ITS Chapter, IEEE Seattle AP/ED/MTT Joint Chapter, and IEEE Vancouver Joint Communications Chapter
Date and time: Thursday, March 11, 2021, at 6 pm
IEEE ComSoc Virtual Distinguished Lecture
This event is co-organized by the IEEE Windsor, Vancouver Joint Communications, Atlanta Central Texas, North Macedonia, Jamaica, Denver, and Italy Chapters.
Date and time: Thursday, February 25, 2021, at 9 am
Biography: Dr. Arslan (IEEE Fellow) has received his BS degree from Middle East Technical University (METU), Ankara, Turkey in 1992; MS and Ph.D. degrees in 1994 and 1998 from Southern Methodist University (SMU), Dallas, TX. USA. From January 1998 to August 2002, he was with the research group of Ericsson Inc., NC, USA, where he was involved with several projects related to 2G and 3G wireless communication systems. Since August 2002, he has been with the Electrical Engineering Dept. of University of South Florida, Tampa, FL, USA, where he is a Professor. In December 2013, he joined Istanbul Medipol University to found the Engineering College, where he has worked as the Dean of the School of Engineering and Natural Sciences. He has also served as the director of the Graduate School of Engineering and Natural Sciences in the same university. In addition, he has worked as a part-time consultant for various companies and institutions including Anritsu Company, Savronik Inc., and The Scientific and Technological Research Council of Turkey. Dr. Arslan’s research interests are related to advanced signal processing techniques at the physical and medium access layers, with cross-layer design for networking adaptivity and QoS control. He is interested in many forms of wireless technologies including cellular radio, wireless PAN/LAN/MANs, fixed wireless access, aeronautical networks, underwater networks, in vivo networks, and wireless sensors networks. His current research interests are on 5G and beyond radio access technologies, physical layer security, interference management (avoidance, awareness, and cancellation), cognitive radio, small cells, powerline communications, smart grid, UWB, multi-carrier wireless technologies, dynamic spectrum access, co-existence issues on heterogeneous networks, aeronautical (High Altitude Platform) communications, channel modeling and system design, and underwater acoustic communications. He has served as technical program committee chair, technical program committee member, session and symposium organizer, and workshop chair in several IEEE conferences. He is currently a member of the editorial board for the IEEE Surveys and Tutorials and the Sensors Journal. He has also served as a member of the editorial board for the IEEE Transactions on Communications, the IEEE Transactions on Cognitive Communications and Networking (TCCN), the Elsevier Physical Communication Journal, the Hindawi Journal of Electrical and Computer Engineering, and Wiley Wireless Communication and Mobile Computing Journal.
DETECT: Wearable Sensor Data to Predict COVID-19 and Viral Illnesses
Date and time: Tuesday, December 15, 2020, from 6 pm to 7:30 pm
Biography: Dr. Giorgio Quer received a Ph.D. degree (2011) in Information Engineering from University of Padova, Italy. In 2007, he was a visiting researcher at the Centre for Wireless Communication at the University of Oulu, Finland. During his Ph.D., he proposed a solution for the distributed compression of wireless sensor networks signals, based on the joint exploitation of Compressive Sensing and Principal Component Analysis. From 2010 to 2016, he was at the Qualcomm Institute, University of California San Diego (UCSD), working on cognitive networks protocols and implementation. At Scripps Research, he is leading the Data Science and Analytics Scripps team involved in the All of Us Research Program (NIH), together with several efforts involving big data and AI in digital medicine, including DETECT, towards the use of wearables to detect COVID-19. He is a Senior Member of the IEEE and a Distinguished Lecturer for the IEEE Communications society. His research interests include wireless sensor networks, probabilistic models, deep convolutional networks, wearable sensors, physiological signal processing, and digital medicine.
Current and Future Trends in 5G/B5G/6G
Presented by: Mehdi Bennis (U. Oulu) Walid Saad (VirginiaTech), Halim Yanikomeroglu (Carleton U), Wei Yu (U Toronto), and Vincent Wong (UBC)
Date and time: Monday, December 14, 2020, from 8:45 am to 12:15 pm
Event details and registration info: https://events.vtools.ieee.org/m/241334
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.
IEEE Seattle and Vancouver Joint Communications Chapter Virtual Holiday Party! With Dan Gelbart as a speaker
Event details and registration info: https://events.vtools.ieee.org/m/247150
Meeting link will be sent at least 2 days in advance of the event to all registrants.
This event is co-organized by IEEE Seattle and Vancouver Joint Communications Chapters.
Presented by: Dr. Dan Gelbart, Rapidia
Date and time: Friday, December 11, 2020, from 6 pm to 8 pm
Abstract: Heaviside and Kao are less known than Shannon and Hamming while their contribution to telecom is of the same magnitude. What was the contribution and how they achieved it makes a fascinating story. The talk will include showing some historical objects from Heaviside’s period.
Biography: Dan Gelbart is a well known Canadian inventor and entrepreneur. He has 128 US patents to his name and was a founder of several successful high tech companies in Canada. Cumulative revenues from these patents are several billion $. Dan was the co-founder of Creo, which revolutionized the printing industry by imaging printing plates thermally instead of photonically. Creo was sold to Kodak in 2005 for a billion $. Dan is also the co-founder of Kardium, a medical device company which has a treatment for atrial fibrillation showing much improved results compared to current treatments. Other successful companies based on technology developed by Dan were MDI (sold to Motorola) and Cymbolic Sciences (sold to Shlumberger). Currently Dan is the president of Rapidia, a company he founded to supply 3D printers for metals. Dan received numerous scientific awards, two honorary doctorates and won several crowd-sourced scientific competitions such as Innocentive. He also has a popular YouTube course on building prototypes. He has an MSc and BSc in Electrical Engineering from the Technion, Israel institute of Technology.
IEEE ComSoc Virtual Distinguished Lecture
Date and time: Monday, November 9, 2020, at 8 am
Abstract: Optimal resource allocation is a fundamental challenge for dense and heterogeneous wireless networks with massive wireless connections. Because of the non-convex nature of the optimization problems, often it is computationally demanding to obtain the optimal resource allocation. Machine learning, especially Deep learning (DL), is a powerful tool where a multi-layer neural network can be trained to model a resource management algorithm using network data. Therefore, resource allocation decisions can be obtained without intensive online computations which would be required otherwise for the solution of resource allocation problems. Recently, deep reinforcement learning (DRL) has emerged as a promising technique in solving non-convex optimization problems. Unlike deep learning (DL), DRL does not require any optimal/near-optimal training dataset which is either unavailable or computationally expensive in generating synthetic data. In this talk, I shall present a supervised DL-based as well as a centralized DRL-based downlink power allocation scheme for a multi-cell system intending to maximize the total network throughput. Specifically, I shall discuss a deep Q-learning (DQL) approach to achieve near-optimal power allocation policy. I shall present some simulation results to compare the proposed DRL-based power allocation scheme with the conventional schemes in a multi-cell scenario.
Biography: Ekram Hossain (F’15) is a Professor in the Department of Electrical and Computer Engineering at University of Manitoba, Canada. He is a Member (Class of 2016) of the College of the Royal Society of Canada. Also, he is a Fellow of the Canadian Academy of Engineering. Dr. Hossain’s current research interests include design, analysis, and optimization beyond 5G/6G cellular wireless networks. He was elevated to an IEEE Fellow “for contributions to spectrum management and resource allocation in cognitive and cellular radio networks”. To date, his research works have received 31,100+ citations (in Google Scholar, with h-index = 91). He received the 2017 IEEE ComSoc TCGCC (Technical Committee on Green Communications & Computing) Distinguished Technical Achievement Recognition Award “for outstanding technical leadership and achievement in green wireless communications and networking”. Dr. Hossain has won several research awards including the “2017 IEEE Communications Society Best Survey Paper Award” and the “2011 IEEE Communications Society Fred Ellersick Prize Paper Award”. He was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science in 2017, 2018, and 2019. Currently he serves as the Editor-in-Chief of IEEE Press and an Editor for the IEEE Transactions on Mobile Computing. Previously, he served as the Editor-in-Chief for the IEEE Communications Surveys and Tutorials (2012-2016). He is a Distinguished Lecturer of the IEEE Communications Society and the IEEE Vehicular Technology Society. Also, he is an elected member of the Board of Governors of the IEEE Communications Society for the term 2018-2020.