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

TL;DR

This paper presents a multi-agent deep reinforcement learning framework for efficient and fair radio resource allocation in 5G RAN, demonstrating real-time improvements in resource utilization and provisioning.

Contribution

It introduces a novel multi-agent DRL approach for RAN resource management in 5G networks using local monitoring data.

Findings

DRL agents improve resource utilization efficiency

Agents ensure fair radio resource allocation

Real-time deployment shows minimized over provisioning

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.