The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network

• URL: http://arxiv.org/abs/2509.10478v1
• Date: Wed, 27 Aug 2025 20:47:45 GMT
• Title: The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network
• Authors: Oluwaseyi Giwa, Michael Adewole, Tobi Awodumila, Pelumi Aderinto,
• Abstract summary: We present the concept of the Large Language Model (LLM)-RAN Operator.<n>LLM is embedded into the RAN control loop to translate high-level human intents into optimal network actions.<n>This paper aims to bridge the gap between AI theory and wireless systems engineering in the NextG era.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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