{"ID":2877808,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10478","arxiv_id":"2509.10478","title":"The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network","abstract":"The management of future AI-native Next-Generation (NextG) Radio Access Networks (RANs), including 6G and beyond, presents a challenge of immense complexity that exceeds the capabilities of traditional automation. In response, we introduce the concept of the LLM-RAN Operator. In this paradigm, a Large Language Model (LLM) is embedded into the RAN control loop to translate high-level human intents into optimal network actions. Unlike prior empirical studies, we present a formal framework for an LLM-RAN operator that builds on earlier work by making guarantees checkable through an adapter aligned with the Open RAN (O-RAN) standard, separating strategic LLM-driven guidance in the Non-Real-Time (RT) RAN intelligent controller (RIC) from reactive execution in the Near-RT RIC, including a proposition on policy expressiveness and a theorem on convergence to stable fixed points. By framing the problem with mathematical rigor, our work provides the analytical tools to reason about the feasibility and stability of AI-native RAN control. It identifies critical research challenges in safety, real-time performance, and physical-world grounding. This paper aims to bridge the gap between AI theory and wireless systems engineering in the NextG era, aligning with the AI4NextG vision to develop knowledgeable, intent-driven wireless networks that integrate generative AI into the heart of the RAN.","short_abstract":"The management of future AI-native Next-Generation (NextG) Radio Access Networks (RANs), including 6G and beyond, presents a challenge of immense complexity that exceeds the capabilities of traditional automation. In response, we introduce the concept of the LLM-RAN Operator. In this paradigm, a Large Language Model (L...","url_abs":"https://arxiv.org/abs/2509.10478","url_pdf":"https://arxiv.org/pdf/2509.10478v1","authors":"[\"Oluwaseyi Giwa\",\"Michael Adewole\",\"Tobi Awodumila\",\"Pelumi Aderinto\"]","published":"2025-08-27T20:47:45Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.LG\",\"eess.SY\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}