5 papers
SIA: Symbolic Interpretability for Anticipatory Deep Reinforcement Learning in Network Control
MohammadErfan Jabbari, Abhishek Duttagupta, Claudio Fiandrino +5
Deep reinforcement learning (DRL) promises adaptive control for future mobile networks but conventional agents remain reactive: they act on past and current measurements and cannot…
SymbXRL: Symbolic Explainable Deep Reinforcement Learning for Mobile Networks
Abhishek Duttagupta, MohammadErfan Jabbari, Claudio Fiandrino +2
The operation of future 6th-generation (6G) mobile networks will increasingly rely on the ability of deep reinforcement learning (DRL) to optimize network decisions in real-time. D…
Spectrum & RAN Sharing: A Measurement-based Case Study of Commercial 5G Networks in Spain
Rostand A. K. Fezeu, Lilian C. Freitas, Eman Ramadan +4
Radio Access Network (RAN) sharing, which often also includes spectrum sharing, is a strategic cooperative agreement among two or more mobile operators, where one operator may use…
On AI Verification in Open RAN
Rahul Soundrarajan, Claudio Fiandrino, Michele Polese +3
Open RAN introduces a flexible, cloud-based architecture for the Radio Access Network (RAN), enabling Artificial Intelligence (AI)/Machine Learning (ML)-driven automation across he…
Opportunities and Challenges for Virtual Reality Streaming over Millimeter-Wave: An Experimental Analysis
Jakob Struye, Hemanth Kumar Ravuri, Hany Assasa +5
Achieving extremely high-quality and truly immersive interactive Virtual Reality (VR) is expected to require a wireless link to the cloud, providing multi-gigabit throughput and ex…