ORION: Intent-Aware Orchestration in Open RAN for SLA-Driven Network Management

2026-03-03

AI Summary

This work addresses the lack of end-to-end automation in Open RAN for translating high-level service intents into low-level configurations, a gap exacerbated by existing orchestration approaches that rely on manual policies and struggle to meet SLA-driven autonomy requirements. To bridge this gap, the authors propose ORION, a novel framework that introduces, for the first time in O-RAN, a hierarchical multi-agent architecture powered by large language models (e.g., GPT-5, Gemini 3 Pro, Claude Opus). ORION integrates the Model Context Protocol with a Semantic Mediation Orchestrator (SMO) translation layer to automatically convert natural language intents into executable policies. Through coordinated rApp/xApp interactions between the Non-RT and Near-RT RICs, ORION enables full lifecycle management—from intent ingestion to E2 closed-loop execution. Experimental results demonstrate 100% policy generation success and a significant reduction in configuration complexity, laying a foundation for autonomous 6G networks.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The disaggregation of the Radio Access Network (RAN) introduces unprecedented flexibility but significant operational complexity, necessitating automated management frameworks. However, current Open RAN (O-RAN) orchestration relies on fragmented manual policies, lacking end-to-end intent assurance from high-level requirements to low-level configurations. In this paper, we propose ORION, an O-RAN compliant intent orchestration framework that integrates Large Language Models (LLMs) via the Model Context Protocol (MCP) to translate natural language intents into enforceable network policies. ORION leverages a hierarchical agent architecture, combining an MCP-based Service Management and Orchestration (SMO) layer for semantic translation with a Non-Real-Time RIC rApp and Near-Real-Time RIC xApp for closed-loop enforcement. Extensive evaluations using GPT-5, Gemini 3 Pro, and Claude Opus demonstrate a 100% policy generation success rate for high-capacity models, highlighting significant trade-offs in reasoning efficiency. We show that ORION reduces provisioning complexity by automating the complete intent lifecycle, from ingestion to E2-level enforcement, paving the way for autonomous 6G networks.

Problem

• Open RAN
• intent orchestration
• SLA-driven management
• network automation
• end-to-end intent assurance

Innovation

• Intent-Based Orchestration
• Large Language Models (LLMs)
• Open RAN
• Model Context Protocol (MCP)
• Autonomous Network Management