🤖 AI Summary
This work addresses the high energy consumption of mobile networks by proposing BeGREEN, an AI-driven intelligent plane within the O-RAN architecture to enable autonomous, energy-efficient radio access network management. By integrating an AI engine with rApps/xApps coordination mechanisms into the O-RAN intelligent plane for the first time, the approach establishes an end-to-end energy efficiency optimization loop that dynamically controls the operational states of simulated cells. Leveraging AI/ML algorithms, the O-RAN intelligent plane framework, and cell state management techniques, the proposed method significantly reduces base station energy consumption in simulation environments, thereby demonstrating the feasibility and effectiveness of AI-driven energy optimization in O-RAN networks.
📝 Abstract
Cellular networks management is being enhanced by O-RAN architecture and AI/ML solutions, enabling automated intelligent control loops for RAN optimization across various use cases. Ensuring energy sustainability is crucial to minimizing the impact of mobile networks on global energy consumption. This demo showcases the BeGREEN Intelligence Plane, an AI-driven solution for energy-efficient management of O-RAN networks. The presented workflow focuses on controlling the operational status of emulated cells, highlighting the integration of key components such as the AI Engine and the optimizations achieved through rApps and xApps