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

Open-Radio Access Network (O-RAN) brings an innovative ap- proach to address the issues of controller placement in large-scale 6G networks. In this work, we introduce a Reinforcement Learn- ing (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 up to 66% lower user latency and up to 14% higher user packet delivery ratio compared to state-of-the-art baselines in a broad range of simulated scenarios.