Ai-powered orchestration improves autonomous 6G mobile network management
Toward Fully Autonomous 6G Networks: AI-driven Operational Efficiency and Optimization
Networking and Internet Architecture
Summary
Mobile networks are becoming more complex as they support many different types of services. The authors look at how future 6G networks can manage themselves more efficiently by using artificial intelligence (AI) built into their radio parts. They propose a new system that can understand high-level goals and balance outside requests with internal rules to run the network autonomously. This approach aims to improve how networks operate and save energy while supporting new business models.
What this means in practice
- •For mobile network operators: Automate the management of 6G radio networks by integrating AI to handle external service requests while maintaining internal policies.
- •For cloud infrastructure developers: Build platforms that support network as a service with AI-driven orchestration to enable flexible multi-tenant access in future mobile infrastructures.
Authors
David Reiss, Oriol Sallent, Miguel Catalan-Cid, Daniel Camps-Mur
Abstract
Mobile networks evolution is characterized by a substantial increase in system complexity, driven by the need to accommodate a growing number of heterogeneous services on top of the digital infrastructure. This growth in service accommodation is expected to accelerate with the adoption of the Network as a Service (NaaS) paradigm, which has emerged as a promising approach to accelerate network innovation while enabling new revenue streams for operators. Although it is fundamental to abstract network capabilities for third-party developers, it poses significant challenges in terms of efficient network operation. To address this increased complexity, future mobile networks are envisioned to be inherently Artificial Intelligence (AI)-native. In particular, the integration of AI within the Radio Access Network (RAN) becomes a key enabler for optimizing operation, energy consumption, and autonomous network control. In this context, this research explores the convergence of AI-native RAN and NaaS ecosystems to enable autonomous 6G RAN management. We propose an Agentic-based orchestration framework capable of interpreting intent-based policies. The proposed framework becomes key to integrate external NaaS requests with internal network management policies.