Events

Low-Earth orbiting (LEO) satellites are now providing broadband service to users around the world. But they face space congestion problems. Some satellites must steer around each other to avoid collisions. In addition, the LEO satellites must share radio spectrum with geosynchronous Earth-orbiting (GEO) satellites and, more interestingly, with each <a href="http://other.This" target="_blank" title="other.This">other.This presentation will touch on collision avoidance but will focus on beam steering and other ways that these satellites can efficiently share spectrum with each other. There are a variety of ways they can do this. Some require information sharing, but others do not. Our work at Carnegie Mellon is examining the effectiveness of various spectrum sharing <a href="http://methods.Co-sponsored" target="_blank" title="methods.Co-sponsored">methods.Co-sponsored by: Lin CaiRoom: 660, Bldg: ECS, University of Victoria, Victoria, British Columbia, Canada, V8P 5C2

Frequency-Domain Cross-Layer Diversity Techniques - Efficient Ways of Coping with Lost Packets in Broadband Wireless SystemsThe design of broadband wireless communications presents considerable challenges. The propagation conditions can be very hostile (e.g., highly dispersive channels and/or deep fading or shadowing effects). This is especially true for systems operating in mm-wave conditions, where one must rely in LoS and/or reflected rays. Moreover, these systems are expected to have power and spectral efficiencies, together with high QoS requirements. There are also implementation complexity constraints, especially at the mobile <a href="http://terminals.Prefix-assisted" target="_blank" title="terminals.Prefix-assisted">terminals.Prefix-assisted block transmission techniques combined with frequency-domain detection are known to be suitable for high rate transmission over severely time-dispersive channels. The most popular modulations based on this concept are OFDM (Orthogonal Frequency-Division Multiplexing) and SC-FDE (Single-Carrier with Frequency-Domain Equalization). However, the severe propagation conditions in multiuser wireless systems make it likely that a non-negligible fraction of the transmitted packets will be lost, either due to deep fading/shadowing effects or due to collisions in the MAC (Medium Access Control) <a href="http://phase.The" target="_blank" title="phase.The">phase.The traditional approach to cope with lost packets is to drop them and ask for its retransmission. However, even packets with a large number of bit errors have useful information on the transmitted blocks that can be employed to improve the detection performance. To take advantage of this, we need to employ a cross-layer approach combining PHY, MAC and LLC layer aspects to cope with lost packets. In this talk we show how we can design powerful cross-layer network diversity techniques specially designed for broadband wireless systems employing block transmission techniques combined with frequency domain <a href="http://detection.Room:" target="_blank" title="detection.Room:">detection.Room: 430, Bldg: EOW, 3800 Finnerty Road, Room 110 Saunders Annex, Victoria, British Columbia, Canada, V8P5C2

Abstract:Artificial intelligence and combinatorial optimization problems—such as drug discovery and prime factorization—remain challenging even for advanced computers. We are attempting to address these limitations by building photonic processors inspired by the brain—photonic neural networks—which utilize light for faster and more energy-efficient processing . We will discuss photonic networks, including Ising machines enabled by thin-film lithium niobate photonics , highlighting their applications in number partitioning, protein folding, wireless communications, and deep learning. Time permitting, we will briefly introduce a quantum photonic neural network that can learn to act as near-perfect components of quantum technologies and discuss the role of weak nonlinearities . Shastri, B.J. et al. Photonics for artificial intelligence and neuromorphic computing. Nature Photonics 15 (2021) Al-Kayed, N. et al. Programmable 200 GOPS Hopfield-inspired photonic Ising machine. Nature 648 (2025) Ewaniuk, J et al. Imperfect quantum photonic neural networks. Advanced Quantum Technologies (2023) .Co-sponsored by: Prof. Nicolas QuesadaSpeaker(s): Bhavin J. ShastriJ. Armand Bombardier J-1035, Polytechnique Montréal, Montréal, Quebec, Canada, H3T 1J4

Zoom Link: <a href="https://ulaval.zoom.us/j/65778451409?pwd=B1j19PbbWPhyXWjxkTf9PjOfIekUCY.1Talk Abstract:Digital twins of the wireless environments offer new capabilities to the communication network design and operation. They could be utilized offline to build site-specific datasets for pre-training and evaluation machine learning models, or online to provide real-time or near real-time priors that aid the various communication system decisions on precoding, channel estimation, spectrum sharing, resource allocation, among many interesting applications. In this talk, I will present key aspects and considerations for modeling, building, calibrating, and utilizing these digital twins to maximize their gains while balancing constraints on cost, latency, and computational overhead. I will also introduce DeepVerse 6G, the world’s first large-scale digital-twin research platform, which provides high-fidelity multi-modal sensing and communication “true” digital twin datasets to accelerate research and development across a wide range of <a href="http://applications.Speaker" target="_blank" title="applications.Speaker">applications.Speaker Biography:Ahmed Alkhateeb received his B.S. and M.S. degrees in Electrical Engineering from Cairo University, Egypt, in 2008 and 2012, and his Ph.D. degree in Electrical and Computer Engineering from The University of Texas at Austin, USA, in 2016. After the Ph.D., he spent some time as a Wireless Communications Researcher at the Connectivity Lab, Facebook, before joining Arizona State University (ASU) in the Spring of 2018, where he is currently an Associate Professor in the School of Electrical, Computer, and Energy Engineering. His research interests are in the broad areas of wireless communications, signal processing, machine learning, and applied math. Dr. Alkhateeb is the recipient of the 2012 MCD Fellowship from The University of Texas at Austin, the 2016 IEEE Signal Processing Society Young Author Best Paper Award for his work on hybrid precoding and channel estimation in millimeter-wave communication systems, and the NSF CAREER Award 2021 to support his research on leveraging machine learning for large-scale MIMO <a href="http://systems.Meeting" target="_blank" title="systems.Meeting">systems.Meeting Link: https://ulaval.zoom.us/j/65778451409?pwd=B1j19PbbWPhyXWjxkTf9PjOfIekUCY.1, Québec City, Quebec, Canada, G1X 4C5

Title: Generative AI and Deep Learning for Resource Allocation in 6G Wireless NetworksAbstract:This talk provides an in-depth exploration of resource management in 6G wireless networks, focusing on the vision, key performance indicators (KPIs), key enabling techniques (KETs), and the diverse array of services characteristic of these advanced networks. The distinct challenges inherent in 6G resource management call for a pivotal shift toward artificial intelligence (AI) and machine learning (ML)–driven solutions, requiring a departure from traditional optimization-centric <a href="http://approaches.The" target="_blank" title="approaches.The">approaches.The talk sheds light on generative AI and unsupervised ML strategies tailored to effectively address convex and non-convex resource management optimization problems. A key focus is placed on deep unsupervised learning techniques for network resource allocation under nonlinear and non-convex constraints. Deep implicit layers and differentiable projection methods are explored as mechanisms to ensure zero constraint violations in applications such as beamforming, phase-shift optimization, and power <a href="http://allocation.Furthermore" target="_blank" title="allocation.Furthermore">allocation.Furthermore, the potential of generative AI models, including large language models (LLMs), to enable proactive network resource allocation is examined, highlighting their role in optimizing performance and reducing reliance on traditional heuristics. The session concludes by identifying key research gaps and future directions, paving the way for next-generation AI-driven wireless <a href="http://networks.Co-sponsored" target="_blank" title="networks.Co-sponsored">networks.Co-sponsored by: Hong-chuan Yang***CANCELED***Room: 660, Bldg: Engineering/Computer Science Building (ECS), 3800 Finnerty Road, Victoria, British Columbia, Canada, V8P 5C2

Title: Generative AI and Deep Learning for Resource Allocation in 6G Wireless NetworksAbstract:This talk provides an in-depth exploration of resource management in 6G wireless networks, focusing on the vision, key performance indicators (KPIs), key enabling techniques (KETs), and the diverse array of services characteristic of these advanced networks. The distinct challenges inherent in 6G resource management call for a pivotal shift toward artificial intelligence (AI) and machine learning (ML)–driven solutions, requiring a departure from traditional optimization-centric <a href="http://approaches.The" target="_blank" title="approaches.The">approaches.The talk sheds light on generative AI and unsupervised ML strategies tailored to effectively address convex and non-convex resource management optimization problems. A key focus is placed on deep unsupervised learning techniques for network resource allocation under nonlinear and non-convex constraints. Deep implicit layers and differentiable projection methods are explored as mechanisms to ensure zero constraint violations in applications such as beamforming, phase-shift optimization, and power <a href="http://allocation.Furthermore" target="_blank" title="allocation.Furthermore">allocation.Furthermore, the potential of generative AI models, including large language models (LLMs), to enable proactive network resource allocation is examined, highlighting their role in optimizing performance and reducing reliance on traditional heuristics. The session concludes by identifying key research gaps and future directions, paving the way for next-generation AI-driven wireless <a href="http://networks.Co-sponsored" target="_blank" title="networks.Co-sponsored">networks.Co-sponsored by: Hong-chuan YangRoom: 660, Bldg: Engineering/Computer Science Building (ECS), 3800 Finnerty Road, Victoria, British Columbia, Canada, V8P 5C2

IEEE North Saskatchewan Section Meeting - May, 202657 Campus Dr, Saskatoon, Saskatchewan, Canada, S7N 5A9, Virtual: https://events.vtools.ieee.org/m/544435

As the semester comes to a close, we will be holding our Annual General Meeting (AGM) on May 6th to elect the 2026–2027 Executive Team of the IEEE BCIT Student <a href="http://Branch.Room:" target="_blank" title="Branch.Room:">Branch.Room: TBD, Bldg: TBD, Burnaby, British Columbia, Canada

Abstract: Large Language Models (LLMs) are emerging as a key enabler for reshaping wireless networks through their powerful reasoning and generalization capabilities. This talk begins with an overview of LLM fundamentals, followed by a discussion of their emerging applications in wireless systems, highlighting both the opportunities they create and the practical challenges they pose. Prompt engineering is introduced as a lightweight and effective alternative to fine-tuning, enabling accurate, context-aware, and resource-efficient decision-making. Two representative use cases will be presented. First, network resource allocation will be addressed through a unified multi-agent framework in which iterative prompting and structured feedback are used to solve constrained non-convex optimization problems, achieving scalable, feasible, and near-optimal performance. Second, intelligent decision-making for autonomous vehicular systems will be discussed through joint optimization of vehicle-to-infrastructure (V2I) communications and autonomous driving policies. Across these applications, LLM-driven frameworks demonstrate reduced time complexity and enhanced adaptability compared to conventional approaches. The talk concludes by outlining how such LLM-driven optimization frameworks can evolve into unified, foundation-model-based engines for end-to-end wireless network <a href="http://intelligence.Biography:" target="_blank" title="intelligence.Biography:">intelligence.Biography: HINA TABASSUM (Senior Member, IEEE) received the Ph.D. degree from the King Abdullah University of Science and Technology. She is currently an Associate Professor with the Lassonde School of Engineering, York University, Canada, where she joined as an Assistant Professor in 2018. She is also appointed as a Visiting Faculty with the University of Toronto in 2024, and the York Research Chair of 5G/6G-enabled mobility and sensing applications in 2023, for five years. She is listed in the Stanford’s list of the World’s Top Two-Percent Researchers from 2021 to 2025. She has been selected as the IEEE ComSoc Distinguished Lecturer for the term 2025–2026. She has co-authored over 120 refereed articles in well-reputed IEEE journals, magazines, and conferences. Her current research interests include multiband 6G wireless communications and sensing networks, connected and autonomous systems, and AI-enabled network mobility and resource management solutions. She has earned numerous distinctions, including the N2Women Star in Networking and Communications (2025), Early Career Lassonde Innovation Award (2023), N2Women Rising Star in Networking and Communications (2022), multiple Exemplary Editor awards from IEEE journals, and appointment to the NSERC Discovery Grant Evaluation Group (2025–2028). She served as an Associate Editor for IEEE COMMUNICATIONS LETTERS from 2019 to 2023, IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY from 2019 to 2023, and IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING from 2020 to 2023. She is also currently serving as an Area Editor for IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY and an Associate Editor for IEEE TRANSACTIONS ON COMMUNICATIONS, IEEE TRANSACTIONS ON MOBILE COMPUTING, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, and IEEE COMMUNICATIONS SURVEYS AND <a href="http://TUTORIALS.Speaker(s):" target="_blank" title="TUTORIALS.Speaker(s):">TUTORIALS.Speaker(s): HINA TABASSUM, Room: EITC E1 270, Winnipeg, Manitoba, Canada

The IEEE Blockchain Forum is returning for the fourth time as part of (https://www.torontotechweek.com/). The goal of this compact one-day event is to congregate BUIDLers, researchers, academics, and engineers building blockchain protocols, infrastructure, and decentralized software <a href="http://applications.Note:" target="_blank" title="applications.Note:">applications.Note: (https://events.vtools.ieee.org/m/469545) counted with 200 participants and speakers from JP Morgan, the Bank of Canada, Mastercard, the Ethereum Enterprise Alliance, EY, Starknet, among <a href="http://others.Co-sponsored">others.[]Co-sponsored by: Toronto Tech Week and the Government of OntarioSpeaker(s): Lawrence Ley, Ken Timsit, Manuel Badel, Srisht Fateh Singh, Revanth Reddy Airre, Ngozi Nora Agwu, Douglas Heintzman, Nisrine Labcir, Griff Green, Jean-Luc Pellerin, Thomas YouAgenda: Morning- Keynotes- Thought leadership talks from industry leaders- In-depth panelsLunch will be providedAfternoon- Technical talks- Workshops- Startups pitches and demosOntario Investment and Trade Centre, 250 Yonge Street, 35th Floor, Toronto, Ontario, Canada, M5B 2L7

IEEE North Saskatchewan Section Meeting - June, 202657 Campus Dr, Saskatoon, Saskatchewan, Canada, S7N 5A9, Virtual: https://events.vtools.ieee.org/m/544436