面向超现代网络的智能体AI：用于RAN自主与保障的多智能体框架
Agentic AI for Ultra-Modern Networks: Multi-Agent Framework for RAN Autonomy and Assurance
AI总结：
针对O-RAN集中式RIC编排的策略冲突与安全风险，论文提出多智能体协作的RAN自主保障框架，并通过流量引导实验证明其可阻断不安全策略、维护全网KPI稳定。
摘要：
Beyond 5G和6G网络日益增长的复杂性，要求采用新的自主与保障范式。传统O-RAN控制环严重依赖基于RIC的编排，这会集中智能，并使系统暴露于策略冲突、数据漂移以及在意外情况下采取不安全行动等风险。在本研究中，我们主张自主网络的未来在于多智能体架构，即由专门的智能体协作完成数据收集、模型训练、预测、策略生成、验证、部署和保障。通过用分布式智能体取代紧耦合的集中式基于RIC工作流，该框架实现自主性、韧性、可解释性和系统级安全。为证实这一愿景，我们设计并评估了一个在激增和漂移条件下的流量引导用例。跨四项KPI（RRC连接用户数、IP吞吐量、PRB利用率和SINR）的结果表明，朴素的预测器驱动部署会改善局部KPI，但会使邻近区域失稳；而智能体系统会阻断不安全策略，从而维护全球网络健康。本研究强调，多智能体架构是下一代RAN中可信AI驱动自主性的可靠基础。
英文摘要：
The increasing complexity of Beyond 5G and 6G networks necessitates new paradigms for autonomy and assur- ance. Traditional O-RAN control loops rely heavily on RIC- based orchestration, which centralizes intelligence and exposes the system to risks such as policy conflicts, data drift, and unsafe actions under unforeseen conditions. In this work, we argue that the future of autonomous networks lies in a multi-agentic architecture, where specialized agents collaborate to perform data collection, model training, prediction, policy generation, verification, deployment, and assurance. By replacing tightly- coupled centralized RIC-based workflows with distributed agents, the framework achieves autonomy, resilience, explainability, and system-wide safety. To substantiate this vision, we design and evaluate a traffic steering use case under surge and drift conditions. Results across four KPIs: RRC connected users, IP throughput, PRB utilization, and SINR, demonstrate that a naive predictor-driven deployment improves local KPIs but destabilizes neighbors, whereas the agentic system blocks unsafe policies, preserving global network health. This study highlights multi- agent architectures as a credible foundation for trustworthy AI- driven autonomy in next-generation RANs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
