Generative AI and Deep Learning for Resource Allocation in 6G Wireless Networks
Title: Generative AI and Deep Learning for Resource Allocation in 6G Wireless Networks
Abstract:
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 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 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 networks.
Date and Time
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- 3800 Finnerty Road
- Victoria, British Columbia
- Canada V8P 5C2
- Building: Engineering/Computer Science Building (ECS)
- Room Number: 660
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- Co-sponsored by Hong-chuan Yang
- Starts 02 February 2026 08:00 AM UTC
- Ends 05 May 2026 07:00 AM UTC
- No Admission Charge
Speakers
Biography:
Dr. Hina Tabassum (Senior Member, IEEE) received the Ph.D. degree from the King Abdullah University of Science and Technology (KAUST). 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 the York Research Chair of 5G/6G-enabled mobility and sensing applications in 2023, for five years. Prior to that, she was a postdoctoral research associate at University of Manitoba, Canada. She has published over 100 refereed papers in well-reputed IEEE journals, magazines, and conferences. She received the Lassonde Innovation Early-Career Researcher Award in 2023 and the N2Women: Rising Stars in Computer Networking and Communications in 2022. She was listed in the Stanford's list of the World’s Top Two-Percent Researchers in 2021, 2022, 2023, and 2024. She has been recognized as an Exemplary Editor by the IEEE Communications Letters (2020), IEEE Open Journal of the Communications Society (IEEE OJCOMS) (2023), and IEEE Transactions on Green Communications and Networking (2023). She was recognized as an Exemplary Reviewer (Top 2% of all reviewers) by IEEE Transactions on Communications in 2015, 2016, 2017, 2019, and 2020. She is the Founding Chair of the Special Interest Group on THz communications in IEEE Communications Society (ComSoc)-Radio Communications Committee (RCC). She served as an Associate Editor for IEEE Communications Letters (2019–2023), IEEE OJCOMS (2019–2023), and IEEE Transactions on Green Communications and Networking (2020–2023). Currently, she is also serving as an Area Editor for IEEE OJCOMS and an Associate Editor for IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, and IEEE Communications Surveys and Tutorials.
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