用智能体AI提升开放无线接入网的自主调控能力
Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence
- 采用计划-执行-观察-反思框架，实现自管理的网络控制
- 在多小区仿真中使资源使用降低8.83%，性能优于传统方法
- 适合需要可解释、可审计的智能网络运维场景
开放无线接入网（O-RAN）在非实时智能控制器（Non-RT RIC）、近实时智能控制器（Near-RT RIC）和分布式单元间暴露了丰富的控制与遥测接口，但同时也增加了多租户、多目标网络在安全与可审计性方面运营的难度。与此同时，具备显式规划、工具调用、记忆与自我管理能力的智能体AI系统，为构建长期运行的控制闭环提供了自然路径。本文综述了如何将此类智能体控制器引入O-RAN：首先回顾O-RAN架构，对比智能体控制器与传统机器学习/强化学习xApps；接着围绕三大任务集群组织分析——网络切片生命周期、无线资源管理（RRM）闭环、跨领域安全、隐私与合规性。随后提出一组基础智能体原语（计划-执行-观察-反思、技能作为工具使用、记忆与证据、自我管理门控），并在多小区O-RAN仿真中验证其效果，结果显示相较基线及移除特定原语的消融实验，切片生命周期与RRM性能均有提升。安全、隐私与合规性被讨论为标准部署中的架构约束与开放挑战。该框架实现了三种经典网络切片下平均8.83%的资源使用降低。
原文摘要 · Abstract (English)
Open RAN (O-RAN) exposes rich control and telemetry interfaces across the Non-RT RIC, Near-RT RIC, and distributed units, but also makes it harder to operate multi-tenant, multi-objective RANs in a safe and auditable manner. In parallel, agentic AI systems with explicit planning, tool use, memory, and self-management offer a natural way to structure long-lived control loops. This article surveys how such agentic controllers can be brought into O-RAN: we review the O-RAN architecture, contrast agentic controllers with conventional ML/RL xApps, and organise the task landscape around three clusters: network slice life-cycle, radio resource management (RRM) closed loops, and cross-cutting security, privacy, and compliance. We then introduce a small set of agentic primitives (Plan-Act-Observe-Reflect, skills as tool use, memory and evidence, and self-management gates) and show, in a multi-cell O-RAN simulation, how they improve slice life-cycle and RRM performance compared to conventional baselines and ablations that remove individual primitives. Security, privacy, and compliance are discussed as architectural constraints and open challenges for standards-aligned deployments. This framework achieves an average 8.83\% reduction in resource usage across three classic network slices.
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