6G时代将通信与AI融合，实现边缘算力共享与协同调度。
Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G
- 构建Open RAN与AI-RAN融合架构，支持软硬解耦与云原生部署。
- 引入AI-RAN Orchestrator，统一管理通信与AI资源分配。
- 支持多时序异构负载，适合运营商拓展智能服务场景。
面向网络边缘日益增长的数据密集型人工智能（AI）应用，亟需重构无线接入网（RAN）设计范式，从被动使用AI优化网络，转向主动支持分布式AI工作负载。这为运营商利用现有基础设施实现AI商业化带来新机遇。本文提出一种新型的开放融合架构，统一编排和管理电信与AI工作负载于共享基础设施上。该架构在开放RAN（O-RAN）的模块化、解耦与云原生原则基础上，扩展支持异构AI部署。核心创新包括：(i) AI-RAN编排器，扩展了O-RAN的服务管理与编排（SMO）功能，实现对RAN与AI工作负载的资源一体化调度；(ii) AI-RAN站点，提供具备实时处理能力的分布式边缘AI平台。所提架构支持灵活编排，满足不同时间尺度下异构工作负载的管理需求，同时保持开放标准化接口与多厂商互操作性。
原文摘要 · Abstract (English)
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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