Digital Twin and AI-Empowered Proactive Radio Resource Management for Vehicular Networks in 6G and Beyond.
#digital-twin
#AI
#resource-management
#vehicular-networks
Due to the rapid emergence of smart, autonomous, and connected vehicles, Vehicular networks in 6G and beyond are entering an era where reactive radio resource control is no longer sufficient. Traditional radio resource management (RRM) suffers from outdated channel measurements, significant overhead and latency in channel state acquisition, and lack of situational awareness. Thus, addressing the extreme dynamics of mobility, blockage, and interference demands a paradigm shift toward predictive, context-aware, and proactive RRM schemes.
In this talk, we present that digital twin (DT) technology is a key enabler to realize this transformation. By maintaining a synchronized, high-fidelity, geo-spatial replica of the physical network, DT allows networks to “see the future” — anticipating channel evolution, traffic demand, and environmental changes before they occur. This capability enables predictive optimization, context-aware decision-making, and real-time what-if analysis under rapidly changing vehicular scenarios.
This talk presents our vision toward DT-in-the-loop proactive RRM, where real-time network data continuously updates the twin, and AI-driven optimization leverages this predictive insight to make proactive control decisions. We will first discuss the design of a geospatially aware, propagation-centric, high-fidelity DT capable of accurately modeling dynamic wireless channels. We will then introduce several DT-in-the-loop optimization frameworks that integrate real-time data from physical networks with predictive modeling to enable proactive and adaptive resource management. These approaches effectively mitigate the impact of outdated channel information, enhance interference management, and improve connectivity reliability. Furthermore, we will demonstrate a DT-based framework for real-time, high-resolution radio environment mapping in dynamic vehicular environments while accounting for time-varying blockages. We will also outline several future research directions toward scalable and practical DT-enabled RRM for 6G and beyond.
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Speakers
Zoheb Hassan of Laval University
Topic:
Digital Twin and AI-Empowered Proactive Radio Resource Management for Vehicular Networks in 6G and Beyond.
Due to the rapid emergence of smart, autonomous, and connected vehicles, Vehicular networks in 6G and beyond are entering an era where reactive radio resource control is no longer sufficient. Traditional radio resource management (RRM) suffers from outdated channel measurements, significant overhead and latency in channel state acquisition, and lack of situational awareness. Thus, addressing the extreme dynamics of mobility, blockage, and interference demands a paradigm shift toward predictive, context-aware, and proactive RRM schemes.
In this talk, we present that digital twin (DT) technology is a key enabler to realize this transformation. By maintaining a synchronized, high-fidelity, geo-spatial replica of the physical network, DT allows networks to “see the future” — anticipating channel evolution, traffic demand, and environmental changes before they occur. This capability enables predictive optimization, context-aware decision-making, and real-time what-if analysis under rapidly changing vehicular scenarios.
This talk presents our vision toward DT-in-the-loop proactive RRM, where real-time network data continuously updates the twin, and AI-driven optimization leverages this predictive insight to make proactive control decisions. We will first discuss the design of a geospatially aware, propagation-centric, high-fidelity DT capable of accurately modeling dynamic wireless channels. We will then introduce several DT-in-the-loop optimization frameworks that integrate real-time data from physical networks with predictive modeling to enable proactive and adaptive resource management. These approaches effectively mitigate the impact of outdated channel information, enhance interference management, and improve connectivity reliability. Furthermore, we will demonstrate a DT-based framework for real-time, high-resolution radio environment mapping in dynamic vehicular environments while accounting for time-varying blockages. We will also outline several future research directions toward scalable and practical DT-enabled RRM for 6G and beyond.
Biography:
Zoheb Hassan is an Assistant Professor in the Department of Electrical and Computer Engineering, Université Laval, Canada. Prior to joining Université Laval, he served as a Senior Postdoctoral Research Fellow at École de Technologie Supérieure (ETS) and as a Research Assistant Professor in the ECE Department at Virginia Tech, USA. Dr. Hassan obtained his doctorate degree from the Electrical and Computer Engineering Department at the University of British Columbia, Vancouver, Canada. He was the recipient of a prestigious Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship grant and recognized as the top-ranked applicant. He has authored and co-authored over 50 journal articles and 35 conference papers in digital twin, radio resource optimization, spectrum sharing, and optical wireless communications in renowned journals and conferences of the IEEE Communications Society. His research is supported by the prestigious research grants of NSERC, Fonds de Recherche Québec (FRQ) and Department of National Defense Canada. He serves/served as an Editor for the IEEE Transactions on Communications, an Associate Editor of IEEE Internet of Things of Journal, and as a guest editor for the IEEE Open Journal of the Communications Society.
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Address:2325 Rue de l'Université, , Québec, Quebec, Canada, G1V 0A6
