![[2602.14117] Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.14117] Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management
Summary
This article presents a multi-scale agentic AI framework for Open Radio Access Networks (O-RAN), enhancing real-time network control and management through coordinated AI agents across various control loops.
Why It Matters
As O-RAN technology evolves, integrating AI for network management becomes crucial for operational efficiency and adaptability. This framework addresses the complexities of disaggregated networks, enabling improved performance and responsiveness in 6G environments.
Key Takeaways
- The proposed framework organizes RAN intelligence into a coordinated hierarchy across different control loops.
- Large Language Models (LLMs) and Small Language Models (SLMs) play pivotal roles in translating operator intent and executing low-latency optimizations.
- A proof-of-concept implementation demonstrates the framework's effectiveness in non-stationary conditions and resource control.
Computer Science > Networking and Internet Architecture arXiv:2602.14117 (cs) [Submitted on 15 Feb 2026] Title:Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management Authors:Hojjat Navidan, Mohammad Cheraghinia, Jaron Fontaine, Mohamed Seif, Eli De Poorter, H. Vincent Poor, Ingrid Moerman, Adnan Shahid View a PDF of the paper titled Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management, by Hojjat Navidan and 7 other authors View PDF HTML (experimental) Abstract:Open Radio Access Networks (O-RAN) promise flexible 6G network access through disaggregated, software-driven components and open interfaces, but this programmability also increases operational complexity. Multiple control loops coexist across the service management layer and RAN Intelligent Controller (RIC), while independently developed control applications can interact in unintended ways. In parallel, recent advances in generative Artificial Intelligence (AI) are enabling a shift from isolated AI models toward agentic AI systems that can interpret goals, coordinate multiple models and control functions, and adapt their behavior over time. This article proposes a multi-scale agentic AI framework for O-RAN that organizes RAN intelligence as a coordinated hierarchy across the Non-Real-Time (Non-RT), Near-Real-Time (Near-RT), and Real-Time (RT) control loops: (i) A Large Language Model (LLM) agent in the Non-RT RIC tra...
