A Multi-Agent Deep Reinforcement Learning Approach for RAN Resource Allocation in O-RAN
Farhad Rezazadeh, Lanfranco Zanzi, Francesco Devoti, Sergio Barrachina‐Muñoz, Engin Zeydan, Xavier Costa‐Pérez, Josep Mangues‐Bafalluy
Abstract
Artificial intelligence (AI) and Machine Learning (ML) are considered as key enablers for realizing the full potential of fifth-generation (5G) and beyond mobile networks, particularly in the context of resource management and orchestration. In this demonstration, we consider a fully-fledged 5G mobile network and develop a multi-agent deep reinforcement learning (DRL) framework for RAN resource allocation. By leveraging local monitoring information generated by a shared gNodeB instance (gNB), each DRL agent aims to optimally allocate radio resources concerning service-specific traffic demands belonging to heterogeneous running services. We perform experiments on the deployed testbed in real-time, showing that DRL-based agents can allocate radio resources fairly while improving the overall efficiency of resource utilization and minimizing the risk of over provisioning.