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

• URL: http://arxiv.org/abs/2507.06911v1
• Date: Wed, 09 Jul 2025 14:49:11 GMT
• Title: Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G
• Authors: Michele Polese, Niloofar Mohamadi, Salvatore D'Oro, Tommaso Melodia,
• Abstract summary: This article presents a novel converged O-RAN and AI-RAN architecture that unifies orchestration and management of both telecommunications and AI workloads on shared infrastructure.<n>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.
• Score: 20.07205081315289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of data-intensive Artificial Intelligence (AI) applications at the network edge demands a fundamental shift in RAN design, from merely consuming AI for network optimization, to actively enabling distributed AI workloads. This paradigm shift presents a significant opportunity for network operators to monetize AI at the edge while leveraging existing infrastructure investments. To realize this vision, this article presents a novel converged O-RAN and AI-RAN architecture that unifies orchestration and management of both 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 system supports flexible deployment options, allowing AI workloads to be orchestrated with specific timing requirements (real-time or batch processing) and geographic targeting. The proposed architecture addresses the orchestration requirements for managing heterogeneous workloads at different time scales while maintaining open, standardized interfaces and multi-vendor interoperability.

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