Description
Date depot: 3 décembre 2024
Titre: Intelligent Digital Twin for 6G Network
Directeur de thèse:
Navid NIKAEIN (Eurecom)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Non defini
Resumé: A Digital Twin (DT) is a virtual representation of a physical entity or process, enabling real-time monitoring, simulation, and optimization. In the context of 6G networks, an Intelligent Digital Twin (IDT) goes beyond conventional DT capabilities by integrating advanced AI and machine learning techniques to model, predict, and optimize complex network behaviors. The IDT is an essential enabler for 6G, offering opportunities for proactive network management,
scenario planning, and performance enhancement.
Generative AI (GenAI) further enhances the capabilities of Digital Twins by enabling them to synthesize insights, generate data-driven predictions, and explore a broader range of optimization scenarios. By leveraging GenAI, IDTs can create highly realistic simulations of network behaviors, identify potential inefficiencies, and propose innovative solutions. GenAI can assist in adaptive resource allocation, energy-efficient network designs, and predictive fault management, thereby aligning with the sustainability goals of 6G. This integration empowers the IDT to become not just a tool for real-time monitoring but also a creative tool in designing and evolving next-generation networks.
The goal of this thesis is to answer the question: “How can Intelligent Digital Twins transform 6G networks into sustainable, high-performance systems that adapt to dynamic conditions?” This work will contribute to the foundation of IDT research and development for 6G, paving the way for smarter, greener, and more adaptive next-generation networks.
Doctorant.e: Yaghoubian Ali