1 citations · 1 across the 5 of their papers we have counts for
5 papers
Optimizing Reinforcement Learning Training over Digital Twin Enabled Multi-fidelity Networks
Hanzhi Yu, Hasan Farooq, Julien Forgeat +4
In this paper, we investigate a novel digital network twin (DNT) assisted deep learning (DL) model training framework. In particular, we consider a physical network where a base st…
Through the telecom lens: Are all training samples important?
Shruti Bothe, Illyyne Saffar, Aurelie Boisbunon +3
The rise of AI in telecommunications, from optimizing Radio Access Networks to managing user experience, has sharply increased data volumes and training demands. Telecom data is of…
Multi-Task Learning as enabler for General-Purpose AI-native RAN
Hasan Farooq, Julien Forgeat, Shruti Bothe +2
The realization of data-driven AI-native architecture envisioned for 6G and beyond networks can eventually lead to multiple machine learning (ML) workloads distributed at the netwo…
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework
Haneya Naeem Qureshi, Usama Masood, Marvin Manalastas +8
The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshoo…
Coordinated Reinforcement Learning for Optimizing Mobile Networks
Maxime Bouton, Hasan Farooq, Julien Forgeat +3
Mobile networks are composed of many base stations and for each of them many parameters must be optimized to provide good services. Automatically and dynamically optimizing all the…