Energy Efficiency Learning Closed-Loop Controls In O-RAN 5G Network

Open Radio Access Network (O-RAN) aims to achieve an open and intelligent RAN architecture that provides greater flexibility, scalability, and network optimization. Machine learning (ML) technologies can play a crucial role in achieving these goals by enabling intelligent decision-making, automated optimization, and proactive maintenance. In this paper, we propose an ML pipeline optimization for energy-efficient deployment of ML-based closed-loop controls (CLC) in 5G O-RAN. Specifically, we propose two ML-based CLCs for resource prediction and network slicing in Non-Realtime RIC and Near-Realtime RIC. We also propose an energy-efficient ML pipeline for dynamically deploying these two CLCs in the O-RAN architecture. Our numerical results demonstrate the effectiveness of our proposed ML pipeline deployment compared to fixed centralized and distributed deployment.