Events

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

Abstract: The next generation of wireless networks will no longer be confined to moving bits — they will sense, communicate, and learn simultaneously. This convergence is anticipated to enable distributed intelligence across devices, unlocking new capabilities for real-time perception and decision-making in dynamic environments. In this talk, two complementary advances in federated signal processing will be presented. First, an over-the-air federated edge learning (OTA-FEEL) framework with integrated radar sensing will be discussed. By leveraging echoes from the environment, rather than treating them solely as interference, robust model aggregation will be maintained while ensuring high-quality sensing and communication performance. A joint scheduling and beamforming design will be presented, supported by low-complexity optimization techniques, to preserve aggregation accuracy under realistic wireless conditions. Second, FedTrack, a novel federated learning–inspired algorithm for distributed target tracking, will be presented. By treating local log-likelihood functions as loss functions in a distributed optimization framework, FedTrack enables devices to collaboratively estimate a moving target’s position and velocity. This communication-efficient method closely approximates centralized maximum likelihood estimation, achieving accuracy near the Cramér–Rao bound while reducing reliance on a central coordinator. Together, these developments illustrate how federated intelligence over the air can transform 6G networks into systems that not only communicate but also sense and learn collaboratively. Implications for autonomous systems, smart cities, and beyond will be discussed, with emphasis on the central role of signal processing innovations in realizing this <a href="http://vision.Room:" target="_blank" title="vision.Room:">vision.Room: MCLD 3038, Bldg: Hector J. MacLeod Building - MCLD, 2356 Main Mall, Vancouver, BC V6T 1Z4, Vancouver, British Columbia, Canada, Virtual: https://events.vtools.ieee.org/m/552228

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

Future of communication and sensing network is being transformed with the advancement in next generations of wireless with Beyond-5G, beyond-WiFi-8, ICAS, NTN, VR/XR/Metaverse, Digital-Twin and other emerging applications. Higher quality of experiences for connected future with ubiquitous lowest latency and superhigh data rate connectivity services will require innovative wireless technologies and communication hardware combined with AI/ML. Mobile platform integrated RF systems with antenna front ends are common factor for most of the wireless applications. Emerging usage scenarios will need intelligent mobile platforms with ultra-small form-factor, requiring co-design and heterogeneous integration of dis-similar semiconductor device, circuit and antenna technologies, in order to satisfy the desired application-specific performance criteria for the evolving use <a href="http://cases.This" target="_blank" title="cases.This">cases.This presentation will present the emerging technology trends and will focus on the antenna-integrated RF to mm-wave/THz array integrated frontend opportunities and challenges demanding new technology, design, development and integration. Example architectures to enablemultifunction microsystem platform will be <a href="http://discussed.Speaker(s):" target="_blank" title="discussed.Speaker(s):">discussed.Speaker(s): Dr. Debabani Choudhury, Agenda: - 6:30 - 7:00 PM Networking- 7:00 - 8:00 PM Technical TalkBldg: Cal Lutheran Center for Entrepreneurship (Hub101), 31416 Agoura Rd, Westlake Village, California, United States, 91361, Virtual: https://events.vtools.ieee.org/m/494694

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: The unprecedented scale, heterogeneity, and performance requirements of 6G networks fundamentally challenge traditional optimization-centric approaches to resource management, motivating a paradigm shift toward artificial intelligence (AI)–driven methodologies. This lecture examines how deep unsupervised learning and generative AI techniques can be leveraged to solve both convex and non-convex network resource allocation problems under complex, nonlinear constraints. Particular emphasis is placed on deep unsupervised learning frameworks, deep implicit layers, and differentiable projection methods that enforce strict constraint satisfaction in applications such as beamforming, phase-shift optimization, and power allocation. The emerging role of generative AI models, including large language models (LLMs), is further discussed in enabling adaptive and environment-aware resource allocation strategies that reduce dependence on frequent model redesign and retraining. The lecture concludes by identifying key research challenges and outlining a roadmap toward scalable, robust, and AI-native 6G wireless <a href="http://networks.Speaker" target="_blank" title="networks.Speaker">networks.Speaker Bio: 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 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.Bldg:" target="_blank" title="Tutorials.Bldg:">Tutorials.Bldg: ICT 424C, University of Calgary, Calgary, Alberta, Canada

Location TBC***CANCELED***Burnaby, British Columbia, Canada

In 1994, Dr. Amy Pinchuk founded InField Scientific Inc., a company specializing in electromagnetic simulation, analysis, and testing for Navy shipboard environments. She has served as the lead industrial Electromagnetic Environmental Effects (E3) subject matter expert for the design and refit of Royal Canadian Navy (RCN) ships and also provides E3 expertise to the Royal New Zealand Navy. InField’s work focuses on optimizing ship design for system performance and Electromagnetic Compatibility (EMC) across mission-critical communication, navigation, and electronic warfare systems. The safety of personnel, ordnance, and fuel is a critical concern in shipboard environments characterized by extremely high electromagnetic fields. This presentation explores the scientific, engineering, and management challenges encountered over three decades in this highly specialized <a href="http://domain.Speaker(s):" target="_blank" title="domain.Speaker(s):">domain.Speaker(s): Dr. Amy Pinchuk, Room: EV003-309, Bldg: Electrical & Computer Engineering Department EV, Concordia University, 1515 Ste. Catherine West, MONTREAL, Quebec, Canada, H3G 1M8

This presentation starts with an overview of the motivation for developing new approaches to breast imaging. Dr. Elise Fear will outline the approaches to imaging that her team has developed using low-power microwaves and the results of recent scans of healthy volunteers and cancer patients. She will also provide perspectives on navigating multiple roles and the importance of mentors, including a reflection on her PhD supervisor, Prof. Maria A. Stuchly. Prof. Stuchly was a faculty member and NSERC Industrial Research Chair at UVic and a respected international authority on <a href="http://bioelectromagnetics.Speaker(s):" target="_blank" title="bioelectromagnetics.Speaker(s):">bioelectromagnetics.Speaker(s): Prof. Elise FearRoom: 660, Bldg: Engineering and Computer Science, University of Victoria, Victoria, 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

La Section IEEE de Québec vous invite à une session spéciale d’une heure avec M. Philippe Dupont et Mme Nathalie Nault de Québec Destination Affaires, l’organisation responsable d’attirer et de soutenir la tenue de conférences internationales dans la région de Qué<a href="http://bec.L’objectif" target="_blank" title="bec.L’objectif">bec.L’objectif de cette rencontre est de présenter aux membres du Comité Exécutif, aux présidents de chapitres techniques, ainsi qu’aux présidents des groupes d’affinité (WIE et YP) les services logistiques, financiers et stratégiques offerts pour faciliter l’organisation de conférences IEEE à Québec. Québec Destination Affaires accompagne les comités organisateurs du début à la fin : préparation des dossiers de candidature, soutien aux visites préalables, coordination avec les infrastructures locales et possibilités d’appui <a href="http://financier.Cette" target="_blank" title="financier.Cette">financier.Cette session est une excellente occasion de mieux comprendre comment un chapitre ou un groupe d’affinité peut transformer ses ateliers, symposiums ou événements en conférences d’envergure, tout en profitant d’un soutien <a href="http://professionnel.Points" target="_blank" title="professionnel.Points">professionnel.Points clés abordés :-Services offerts aux organisateurs de conférences IEEE-Soutien stratégique et financier pour attirer des événements internationaux-Processus de mise en candidature et accompagnement-Exemples de collaborations réussies à Québec-Période de questionsDétails pratiques :-Date : 8 mai 2026-Heure : 12h00 à 13h00 (heure de l’Est)-Format : En personne à Québec Destination Affaires (900, boulevard René-Lévesque est, 2e étage Québec, QC, G1R 2B5)-Coût : Gratuit, lunch fourni par QDA (merci!)-Public cible : Présidents de chapitres techniques, présidents des groupes d’affinité (WIE, YP) et membres du Comité Exécutif de la Section IEEE de Québec. Tout membre intéressé à organiser une conférence IEEE à Québec est le <a href="http://bienvenu.900" target="_blank" title="bienvenu.900">bienvenu.900, boulevard René-Lévesque est, 2e étage, Québec, Quebec, Canada, G1R 2B5