From RAN Control to Agentic Intelligence: Architecture and Vision for Energy Efficient AI-RAN

2026-06-20

AI Summary

This work addresses the surge in energy consumption arising from the deep integration of artificial intelligence (AI) and radio access networks (RAN) in the 6G era, a challenge exacerbated by the lack of cross-application adaptive energy-saving coordination mechanisms in current O-RAN architectures. To bridge this gap, the paper proposes an AI-native RAN architecture that, for the first time, introduces agent-based paradigms and semantic intent abstraction into RAN control. By harmonizing O-RAN’s structured framework with the unified vision of AI-RAN, the proposed approach leverages a large language model (LLM)-driven coordination mechanism to enable adaptive orchestration of heterogeneous workloads, multi-objective optimization, and resolution of cross-application conflicts. Experimental results demonstrate that this method significantly enhances resource utilization efficiency and effectively reduces RAN energy consumption, offering a key enabler for sustainable 6G networks.

Abstract

Future 6G networks will rely on highly distributed, AI-native Radio Access Networks (RANs), where communication and AI workloads share a common infrastructure. This evolution, combined with increasing deployment density and continuous AI processing, is expected to significantly increase RAN energy consumption. While Open RAN (O-RAN) introduces a programmable and modular control framework through the RAN Intelligent Controller (RIC) and Service Management and Orchestration (SMO), current approaches remain largely policy-driven, limiting adaptive energy-aware coordination across multiple applications. In parallel, AI-RAN promotes the convergence of AI and RAN infrastructures through AI-for-RAN, AI-on-RAN, and AI-and-RAN paradigms, yet efficient mechanisms to jointly orchestrate performance, latency, and energy remain an open challenge. This article proposes an agentic AI-native RAN architecture that bridges O-RAN's structured control with AI-RAN's unified vision. Leveraging semantic intent abstraction and Large Language Model (LLM)-driven coordination, the framework enables adaptive orchestration, conflict resolution, and energy-aware multi-objective optimization across heterogeneous workloads. Through representative AI-for-RAN and AI-on-RAN use cases, we show how such coordination can improve resource efficiency and reduce operational energy consumption, paving the way toward sustainable 6G networks.

Problem

• AI-RAN
• energy efficiency
• 6G networks
• RAN Intelligent Controller
• multi-objective optimization

Innovation

• Agentic AI
• AI-RAN
• Energy Efficiency
• LLM-driven Coordination
• Semantic Intent Abstraction