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

arXiv: https://arxiv.org/abs/2507.06911 · HTML: https://mlsanity.com/paper/2507.06911/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 9 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [AI-RAN: Transforming RAN with AI-driven Computing Infrastructure](https://mlsanity.com/paper/2501.09007/) (2025)
- [dApps: Enabling Real-Time AI-Based Open RAN Control](https://mlsanity.com/paper/2501.16502/) (2025)
- [X5G: An Open, Programmable, Multi-vendor, End-to-end, Private 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface](https://mlsanity.com/paper/2406.15935/) (2024)
- [AutoRAN: Automated and Zero-Touch Open RAN Systems](https://mlsanity.com/paper/2504.11233/) (2025)
- [Hardware Acceleration for Open Radio Access Networks: A Contemporary Overview](https://mlsanity.com/paper/2305.09588/) (2023)
- [The Interplay of AI-and-RAN: Dynamic Resource Allocation for Converged 6G Platform](https://mlsanity.com/paper/2503.07420/) (2025)
- [Distributed AI Platform for the 6G RAN](https://mlsanity.com/paper/2410.03747/) (2024)

## Built on by (most cited first, 10 of 10)

- [6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence](https://mlsanity.com/paper/2605.01546/) (2026)
- [CoVSpec: Efficient Device-Edge Co-Inference for Vision-Language Models via Speculative Decoding](https://mlsanity.com/paper/2605.02218/) (2026)
- [TeleResilienceBench: Quantifying Resilience for LLM Reasoning in Telecommunications](https://mlsanity.com/paper/2605.09929/) (2026)
- [AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation](https://mlsanity.com/paper/2606.08173/) (2026)
- [Hybrid Federated Learning for Noise-Robust Training](https://mlsanity.com/paper/2601.04483/) (2026)
- [SLA-Aware Distributed LLM Inference Across Device-RAN-Cloud](https://mlsanity.com/paper/2602.23722/) (2026)
- [From RAN Control to Agentic Intelligence: Architecture and Vision for Energy Efficient AI-RAN](https://mlsanity.com/paper/2606.21955/) (2026)
- [AI-RAN on NPUs: Baseband Processing Without Baseband Chips](https://mlsanity.com/paper/2607.04224/) (2026)
- [CRAFT: Fine-Tuning Pre-hoc Explainability in AI-native 6G RAN](https://mlsanity.com/paper/2609.00590/) (2026)
- [Fresh Memory, Stale Plans: Dependency-Scoped Validation for Distributed LLM-Agent Memory](https://mlsanity.com/paper/2609.03340/) (2026)

## Abstract (arXiv)

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.