DRIVING AI-NATIVE RAN INNOVATION WITH THE SIONNA RESEARCH KIT Dr. Sebastian Cammerer, Senior Research Scientist, NVIDIA Registration Link: []https://events.vtools.ieee.org/m/517965 Host: IEEE (mailto:messaoud.ahmed.ouameur@uqtr.ca?subject=From%20Propagation%20Models%20to%20Physics-Based%20Digital%20Twins%20of%20Emerging%20Wireless%20Communication%20Systems%20-%20Saint%20Maurice%20Sect%20Chap,%20COM19) When: December 2nd at 10H30 AM EST Via zoom: https://uqtr.zoom.us/j/81521084215?pwd=bchQDndZg7DTlpVuaeag6bhGwaOvn9.1 Meeting ID: 815 2108 4215 Password: 018477 ABSTRACT AI will become a cornerstone of future wireless communication systems, enabling radio access networks (RANs) that dynamically adapt to specific radio frequency (RF) environments and enhance their performance even after deployment. Novel paradigms such as end-to-end learning for pilotless transmissions and semantic communications add to the transformative potential of AI. Integrating neural network components into the signal processing pipeline of wireless transceivers poses research challenges, particularly in meeting the stringent, often sub-millisecond, inference latency required by RANs. As such, the full potential of AI-native RANs depends on three main factors: (a) the development of robust software tools, (b) the deployment of specialized hardware platforms for real-time AI acceleration, and (c) the design of fundamentally new transceiver algorithms. In this talk, we outline a path toward prototyping an AI-native RAN using the Sionna Research Kit—an open-source platform designed for development, training, and deployment of AI-native wireless communication systems. We present a 5G NR-compliant real-time neural receiver connected to commercial user equipment, demonstrating how research ideas can be rapidly transformed into over-the-air prototypes using open-source tools. To foster collaboration and accelerate progress in the field, all experiments and results will be made openly available, lowering the barrier to entry and enabling researchers worldwide to translate their ideas into real-world wireless communication systems. BIOGRAPHY Dr. Sebastian Cammerer is a Senior Research Scientist at NVIDIA, working at the intersection of wireless communications and machine learning. He is one of the core developers and maintainers of the Sionna open-source link-level simulator. Before joining NVIDIA, he received his PhD in Electrical Engineering and Information Technology from the University of Stuttgart, Germany. His main research interests are machine learning for wireless communications and channel coding. His work has been recognized with several awards, including the VDE ITG Dissertationspreis 2022, the IEEE SPS Young Author Best Paper Award 2019, and third prize in the Nokia Bell Labs Prize 2019. Speaker(s): Dr. Cammerer, Virtual: https://events.vtools.ieee.org/m/517965

The IEEE APS Student Branch Chapter is back for one last event in this executive year! It has truly been our honor to serve you over the past year. We’re proud to have wrapped up this term with multiple invited talks, including an APS Distinguished Lecturer and an industry speaker, along with social events and, of course, our annual Go tournament! Now it’s time for a new executive team to take the lead and continue growing this chapter. With that, we will be holding the UofT IEEE APS Student Branch Chapter Election on Thursday, December 4th, at 11:30 AM in BA 7180. Since this is our first event of the semester, we’re also excited to welcome the new members of the EM group. It’ll be a great chance for everyone, new and returning, to meet, reconnect, and enjoy lunch together. Call for Executive Members: Elections mean you can become part of the next executive team. The available positions are: Chair Vice-Chair Treasurer and Web-master Secretary This is a fantastic opportunity to gain leadership experience, organize technical and social events, network with experts in the field, strengthen your CV, and help grow the chapter. Executives are also eligible to apply for a travel grant to attend the Chapter Chairs’ Meeting held during the MAPCON conference. If you’re interested in running for any of these positions (one or more), please email hanieh.kianiamiri@mail.utoronto.ca Whether you’re running, supporting a friend, or simply joining us for lunch and good company, you’re warmly welcome to attend. If you have any dietary restrictions, please let us know in advance. We look forward to seeing you all there! https://edu.ieee.org/ca-uotaps/home/ Room: BA7180, Bldg: Bahen Centre for Information Technology, 40 St. George Street Toronto, ON, M5S 2E4, Toronto, Ontario, Canada, M5S 2E4

Abstract: This talk will provide an overview on the recent research on multi-agent systems operating in hostile environments. In the context of consensus problems, we will focus on the influence of misbehaving agents capable to inject false data in their transmissions and how to mitigate such cyber attacks by the approach of the so-called mean subsequence reduced algorithms and their variants. Agents equipped with such algorithms will ignore their neighbors taking outlying state values. We will see that characterizations on the properties necessary for network topologies can be established, and moreover that network resiliency can be enhanced when more communication and computational resources are available. This approach originates in the area of distributed algorithms in computer science, but recent studies in systems control have brought notable advances. We will further discuss extensions of such algorithms to problems of averaging, parameter estimation, and clock synchronization in wireless sensor networks. Speaker(s): Hideaki, Agenda: Abstract: This talk will provide an overview on the recent research on multi-agent systems operating in hostile environments. In the context of consensus problems, we will focus on the influence of misbehaving agents capable to inject false data in their transmissions and how to mitigate such cyber attacks by the approach of the so-called mean subsequence reduced algorithms and their variants. Agents equipped with such algorithms will ignore their neighbors taking outlying state values. We will see that characterizations on the properties necessary for network topologies can be established, and moreover that network resiliency can be enhanced when more communication and computational resources are available. This approach originates in the area of distributed algorithms in computer science, but recent studies in systems control have brought notable advances. We will further discuss extensions of such algorithms to problems of averaging, parameter estimation, and clock synchronization in wireless sensor networks. Room: SF B560 , 172 St. George St.,, Toronto, Ontario, Canada, M5R 0A3

Feko offers state-of-the-art optimization engines to automatically optimize the antenna design and determine the optimum solution. This talk demonstrates the complete workflow to setup optimization in Feko along with the option of specifying a goal directly or using an optimization mask. A workflow to setup a parametric sweep and plotting different outputs against the parametric variables will also be presented. Speaker(s): Gopinath Gampala, Dr. C.J. Reddy Virtual: https://events.vtools.ieee.org/m/519226

[] Zoom Link: https://ulaval.zoom.us/j/69550214937?pwd=iH5u8TmnfzeZUxEc4LMpU1IupTAwvh.1 Time: 12 December 2025, 11.00 AM EST to 12.00 PM EST Talk Abstract: The reliable operation of smart grids increasingly relies on wireless communication links deployed within high-voltage substations and distribution infrastructures. However, these environments are dominated by severe electromagnetic interference (EMI), producing bursty, high-amplitude impulsive noise with strong temporal correlation. Conventional transceiver design based on simple clipping/blanking, or memoryless soft decoding fails to ensure reliable connectivity under realistic EMI, resulting in critical degradation of QoS. This talk presents promising EMI-aware transceiver architectures that bridge theoretical modeling and practical resilience. We first revisit EMI characterization in smart grids, highlighting the impulsive, bursty, and dynamic nature of EMI. We then explore transceiver design strategies ranging from enhanced LLR-based detection to AI-driven architectures. Finally, we present fully AI-native deep semantic transceivers that jointly optimize encoding, decoding, and noise mitigation, demonstrating robust communication in presence of strong EMI. Speaker Biography: Georges Kaddoum is a professor and the research director of the Resilient Machine Learning Institute (ReMI) at École de Technologie Supérieure (ÉTS), Université du Québec, Montréal, Canada. He also holds an industrial research chair and a Tier 2 Canada Research Chair. He earned his Ph.D. in Signal Processing and Telecommunications with High Honors from the National Institute of Applied Sciences (INSA), University of Toulouse, France, in 2009. His research focuses on wireless communication networks, tactical communications, resource allocation, and network security. Prof. Kaddoum is a member of the Royal Society of Canada and has received multiple prestigious recognitions. He has served as an associate editor for IEEE Transactions on Information Forensics and Security and IEEE Communications Letters. Currently, he is an area editor for IEEE Transactions on Machine Learning in Communications and Networking and an editor for IEEE Transactions on Communications. Meeting Link: https://ulaval.zoom.us/j/69550214937?pwd=iH5u8TmnfzeZUxEc4LMpU1IupTAwvh.1, Quebec City, Quebec, Canada

Integrated QoS, Energy-Aware Traffic Engineering, and Weather-Resilient Optical Downlink Scheduling for LEO Satellite Networks Abstract: Low Earth orbit (LEO) mega-constellations are becoming a key component of the future Internet, offering global coverage and supporting applications with diverse performance requirements. Managing traffic in these dynamic networks requires approaches that consider routing, quality of service (QoS), energy efficiency, and environmental factors. Advances in QoS-aware routing, green segment-routed traffic engineering, and weather-resilient optical downlink scheduling show how LEO systems can better adapt to variable loads, heterogeneous QoS demands, and adverse conditions. We divide the presentations in three parts focusing on QoS-driven, energy-aware, and weather-resilient traffic management in LEO satellite networks as follows: Part I: Topology-Aware and QoS-Driven Traffic Management in LEO Satellite Networks Part II: Green Traffic Engineering for Satellite Networks using SRv6, Flex-Algo, and SDN Part III: Optical Downlink Scheduling in Adverse Weather Conditions for Delay-Tolerant Sparse Satellite Constellations. ------------------------------------------------------------------------ Qualité de service intégrée, ingénierie du trafic écoénergétique et planification de la liaison descendante optique résistante aux intempéries pour les réseaux de satellites LEO Résumé: Les méga-constellations en orbite terrestre basse (LEO) deviennent un élément clé de l’Internet du futur, offrant une couverture mondiale et prenant en charge des applications aux exigences de performance variées. La gestion du trafic dans ces réseaux dynamiques nécessite des approches qui prennent en compte le routage, la qualité de service (QoS), l’efficacité énergétique et les facteurs environnementaux. Les progrès réalisés en matière de routage prenant en compte la QoS, d’ingénierie du trafic par segments verts et de planification des liaisons descendantes optiques résistantes aux intempéries montrent comment les systèmes LEO peuvent mieux s’adapter aux charges variables, aux exigences de QoS hétérogènes et aux conditions difficiles. Nous divisons les présentations en trois parties, axées sur la gestion du trafic axée sur la QoS, l’efficacité énergétique et la résilience aux intempéries dans les réseaux de satellites LEO. Partie I : Gestion du trafic prenant en compte la topologie et la QoS dans les réseaux de satellites LEO Partie II : Ingénierie du trafic vert pour les réseaux satellitaires utilisant SRv6, Flex-Algo et SDN Partie III : Planification des liaisons descendantes optiques dans des conditions météorologiques défavorables pour les constellations satellitaires clairsemées tolérantes aux délais. Dhiraj Bhattacharjee (Carleton U) Jiantao Liang (Carleton U) Ethan Fettes (Carleton U) About / A propos The High Throughput and Secure Networks (HTSN) Challenge program is hosting regular virtual seminar series to promote scientific information sharing, discussions, and interactions between researchers. https://nrc.canada.ca/en/research-development/research-collaboration/programs/high-throughput-secure-networks-challenge-program Le programme Réseaux Sécurisés à Haut Débit (RSHD) organise régulièrement des séries de séminaires virtuels pour promouvoir le partage d’informations scientifiques, les discussions et les interactions entre chercheurs. https://nrc.canada.ca/fr/recherche-developpement/recherche-collaboration/programmes/programme-defi-reseaux-securises-haut-debit NEW: In order to promote more open discussions/interactions, at the end of the presentation and Q/A, we will allow other experts in this field (quantum comm) to present very briefly their work (1 slide, 2 min max) or their company. / Afin de favoriser des discussions/interactions plus ouvertes, à la fin de la présentation et des questions/réponses, nous permettrons aux experts de ce domaine (communications quantiques) de présenter très brièvement leurs travaux (1 diapositive, 2 min max) ou leur compagnie. Co-sponsored by: National Research Council, Canada. Speaker(s): Dhiraj Bhattacharjee, Jintao Liang, Ethan Fettes Virtual: https://events.vtools.ieee.org/m/518590

In this seminar, we explore the potential role of digital twins (DTs) in Edge Intelligence (EI)-empowered Integrated Sensing and Communication (ISAC) with two case studies. First, we show that DTs can be used to model the stochastic spatial distributions of sensing targets, which is essential for characterizing service demands and optimizing proactive resource management in ISAC. Our DT design adaptively synergizes multiple candidate spatial models for location-based resource reservation. Second, we show that DTs can enable a user-centric approach to deep neural networks (DNN)-based sensing data processing. Given an ISAC device with a small DNN model and a mobile edge computing (MEC) server with a large DNN model, our DT design supports continual learning in the presence of data drifts. Leveraging the above role of DT, we can achieve objectives such as minimizing resource reservation or computation costs subject to performance constraints. Room: 460, Bldg: ENG, 245 church St., Toronto, Ontario, Canada, M5B 2R2