Deep Learning and Generative AI for Wireless Networks Resource Management
The unprecedented scale, heterogeneity, and performance requirements of future 6G wireless networks are fundamentally challenging traditional optimization-centric approaches to resource management. These challenges are motivating a paradigm shift toward artificial intelligence (AI)–driven wireless optimization and decision-making frameworks. This lecture explores how deep unsupervised learning and generative AI techniques can be leveraged to solve both convex and non-convex resource allocation problems under complex nonlinear constraints. Particular emphasis is placed on deep unsupervised learning architectures, deep implicit layers, and differentiable projection methods that enable constraint-aware optimization in applications such as beamforming, phase-shift optimization, and power allocation. The lecture further examines the emerging role of generative AI models, including large language models (LLMs), in enabling adaptive, scalable, and environment-aware wireless resource management strategies that reduce dependence on frequent model redesign and retraining. The lecture concludes by discussing key open research challenges and outlining a roadmap toward robust, scalable, and AI-native wireless intelligence for future 6G networks and beyond.
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- Dipartimento di Ingegneria dell'Informazione, Elettronica e Telecomunicazioni (DIET)
- via Eudossiana 18
- Roma, Lazio
- Italy 00184
- Room Number: Sala Lettura 2° piano
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- Co-sponsored by Sapienza University of Rome
Speakers
Hina Tabassum of York University, Canada
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 at York University, Canada, where she joined in 2018. She also serves as a Visiting Faculty member at University of Toronto and holds the York Research Chair in 5G/6G-enabled mobility and sensing applications. She was selected as an IEEE Communications Society Distinguished Lecturer for 2025–2026 and has been listed among Stanford’s World’s Top 2% Researchers from 2021 to 2025. 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), and multiple Exemplary Editor awards from IEEE journals. She has authored over 120 refereed publications in leading IEEE journals and conferences. Her research focuses on 5G/6G wireless communications and sensing networks, connected and autonomous systems, and AI-enabled wireless resource management. Her recognitions include the N2Women Star in Networking and Communications (2025), the N2Women Rising Star Award (2022), and the Early Career Lassonde Innovation Award (2023). She currently serves as an editor for several leading IEEE journals, including IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, and IEEE Communications Surveys and Tutorials.