用多智能体架构提升6G无线网络自主性与安全，避免传统系统隐患。
Agentic AI for Ultra-Modern Networks: Multi-Agent Framework for RAN Autonomy and Assurance
- 设计分布式多智能体框架，分工完成数据采集、策略生成与验证。
- 在流量突增和漂移场景下，4项关键指标均优于传统方法。
- 适合研究下一代无线网络自治与可信AI的科研人员。
Beyond 5G和6G网络日益复杂，亟需新的自治与保障范式。传统基于RIC的O-RAN控制环路高度依赖集中式智能，易引发策略冲突、数据漂移及未知条件下不安全行为等风险。本文主张未来自治网络应采用多智能体架构，由专业智能体协作完成数据收集、模型训练、预测、策略生成、验证、部署与保障。通过将紧密耦合的中心化RIC流程替换为分布式智能体，该框架实现了自治、弹性、可解释性与全网安全。为验证这一愿景，我们设计并评估了在流量突增与漂移条件下的流量调度用例。结果表明，在RRC连接用户数、IP吞吐量、物理资源块利用率和信噪比（SINR）四项KPI上，基于预测的简单部署虽提升局部性能，却导致邻区失稳；而智能体系统能有效阻断不安全策略，维护全局网络健康。本研究证明多智能体架构是可信AI驱动的下一代无线接入网自治的可靠基础。
原文摘要 · Abstract (English)
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.
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