Publications (9)
Generalization in Reinforcement Learning for Radio Access Networks
Burak Demirel, Yu Wang, Cristian Tatino +1
Modern RAN operate in highly dynamic and heterogeneous environments, where hand-tuned, rule-based RRM algorithms often underperform. While RL can surpass such heuristics in constra…
Design Principles for Model Generalization and Scalable AI Integration in Radio Access Networks
Pablo Soldati, Euhanna Ghadimi, Burak Demirel +3
Artificial intelligence (AI) has emerged as a powerful tool for addressing complex and dynamic tasks in radio communication systems. Research in this area, however, focused on AI s…
From Intents to Actions: Agentic AI in Autonomous Networks
Burak Demirel, Pablo Soldati, Yu Wang
Telecommunication networks are increasingly expected to operate autonomously while supporting heterogeneous services with diverse and often conflicting intents -- that is, performa…
Modular design of jointly optimal controllers and forwarding policies for wireless control
Burak Demirel, Zhenhua Zou, Pablo Soldati +1
We consider the joint design of packet forwarding policies and controllers for wireless control loops where sensor measurements are sent to the controller over an unreliable and en…
Practical Policy Distillation for Reinforcement Learning in Radio Access Networks
Sara Khosravi, Burak Demirel, Linghui Zhou +2
Adopting artificial intelligence (AI) in radio access networks (RANs) presents several challenges, including limited availability of link-level measurements (e.g., CQI reports), st…
Learning Radio Resource Management in 5G Networks: Framework, Opportunities and Challenges
Francesco Davide Calabrese, Li Wang, Euhanna Ghadimi +2
In the fifth generation (5G) of mobile broadband systems, Radio Resources Management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated R…