ORION: Intent-Aware Orchestration in Open RAN for SLA-Driven Network Management
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914368020545536 |
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| author | Machado, Gabriela da Silva Bruno, Gustavo Z. Huff, Alexandre Brito, Jose Marcos Camara Both, Cristiano B. |
| author_facet | Machado, Gabriela da Silva Bruno, Gustavo Z. Huff, Alexandre Brito, Jose Marcos Camara Both, Cristiano B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03667 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ORION: Intent-Aware Orchestration in Open RAN for SLA-Driven Network Management Machado, Gabriela da Silva Bruno, Gustavo Z. Huff, Alexandre Brito, Jose Marcos Camara Both, Cristiano B. Networking and Internet Architecture C.2.3; C.2.1 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. |
| title | ORION: Intent-Aware Orchestration in Open RAN for SLA-Driven Network Management |
| topic | Networking and Internet Architecture C.2.3; C.2.1 |
| url | https://arxiv.org/abs/2603.03667 |