Data-Driven Orchestration for Distributed RAN Intelligent Controller Placement in 6G Networks

Abstract

Open-Radio Access Network ( O-RAN ) brings an innovative approach to address the issues of controller placement in large-scale 6G networks. In this work, we introduce a Reinforcement Learning (RL ) algorithm for decentralized RAN Intelligent Controller Orchestration in 6G Networks, which leverages the online learning capabilities of a multi-agent RL system. Our method achieves around 42-66% lower user latency and 9-14% higher user packet delivery ratio compared to state-of-the-art baselines in a broad range of simulated scenarios.