20 citations · 22 across the 4 of their papers we have counts for
5 papers
RRM-GPT: A Framework and Vision for Radio Resource Management Foundation Models
Ahmed Aboulfotouh, Akram Bin Sediq, Koosha Pourtahmasi Roshandeh +4
Learning-based models for radio resource management (RRM) are typically built for a single function and deployment, so each new setting repeats the development pipeline. RRM decisi…
SafeSlice: Enabling SLA-Compliant O-RAN Slicing via Safe Deep Reinforcement Learning
Ahmad M. Nagib, Hatem Abou-Zeid, Hossam S. Hassanein
Deep reinforcement learning (DRL)-based slicing policies have shown significant success in simulated environments but face challenges in physical systems such as open radio access…
Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach
Ahmad M. Nagib, Hatem Abou-Zeid, Hossam S. Hassanein
The open radio access network (O-RAN) architecture supports intelligent network control algorithms as one of its core capabilities. Data-driven applications incorporate such algori…
How Does Forecasting Affect the Convergence of DRL Techniques in O-RAN Slicing?
Ahmad M. Nagib, Hatem Abou-Zeid, Hossam S. Hassanein
The success of immersive applications such as virtual reality (VR) gaming and metaverse services depends on low latency and reliable connectivity. To provide seamless user experien…
Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks
Ahmad M. Nagib, Hatem Abou-zeid, Hossam S. Hassanein
Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the wireless networks domain. They are considered promising approaches for solving dynamic radio…