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arXiv · 2507.06911

Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G

Michele Polese·Niloofar Mohamadi·Salvatore D'Oro·Leonardo Bonati·Tommaso Melodia
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Abstract

Data-intensive Artificial Intelligence (AI) applications at the network edge demand a fundamental shift in Radio Access Network (RAN) design, from merely consuming AI for network optimization, to actively enabling distributed AI workloads. This presents a significant opportunity for network operators to monetize AI while leveraging existing infrastructure. To realize this vision, this article presents a novel converged O-RAN and AI-RAN architecture for unified orchestration and management of telecommunications and AI workloads on shared infrastructure. The proposed architecture extends the Open RAN principles of modularity, disaggregation, and cloud-nativeness to support heterogeneous AI deployments. We introduce two key architectural innovations: (i) the AI-RAN Orchestrator, which extends the O-RAN Service Management and Orchestration (SMO) to enable integrated resource and allocation across RAN and AI workloads; and (ii) AI-RAN sites that provide distributed edge AI platforms with real-time processing capabilities. The proposed architecture enables flexible orchestration, meeting requirements for managing heterogeneous workloads at different time scales while maintaining open, standardized interfaces and multi-vendor interoperability.

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BibTeXRIS

Michele Polese, Niloofar Mohamadi, Salvatore D'Oro, Leonardo Bonati, Tommaso Melodia. 2025-07-09. Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G. https://arxiv.org/abs/2507.06911

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