Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917116909715456 |
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| author | Polese, Michele Mohamadi, Niloofar D'Oro, Salvatore Bonati, Leonardo Melodia, Tommaso |
| author_facet | Polese, Michele Mohamadi, Niloofar D'Oro, Salvatore Bonati, Leonardo Melodia, Tommaso |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06911 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G Polese, Michele Mohamadi, Niloofar D'Oro, Salvatore Bonati, Leonardo Melodia, Tommaso Networking and Internet Architecture Artificial Intelligence Signal Processing 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. |
| title | Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G |
| topic | Networking and Internet Architecture Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2507.06911 |