A Multi-Agent Deep Reinforcement Learning Approach for RAN Resource Allocation in O-RAN

Farhad Rezazadeh; Lanfranco Zanzi; Francesco Devoti; Sergio Barrachina-Munoz; Engin Zeydan; Xavier Costa-Pérez; Josep Mangues-Bafalluy

arXiv · 2307.02414

A Multi-Agent Deep Reinforcement Learning Approach for RAN Resource Allocation in O-RAN

Farhad Rezazadeh·Lanfranco Zanzi·Francesco Devoti·Sergio Barrachina-Munoz·Engin Zeydan·Xavier Costa-Pérez·Josep Mangues-Bafalluy

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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.

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Farhad Rezazadeh, Lanfranco Zanzi, Francesco Devoti, Sergio Barrachina-Munoz, Engin Zeydan, Xavier Costa-Pérez, Josep Mangues-Bafalluy. 2023-07-03. A Multi-Agent Deep Reinforcement Learning Approach for RAN Resource Allocation in O-RAN. https://arxiv.org/abs/2307.02414

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