智能体人工智能-RAN：实现意图驱动、可解释和自我进化的开放式RAN智能
Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence
AI总结：
探讨如何将智能体控制器引入开放式RAN，介绍相关架构，对比传统ML/RL xApps，围绕网络切片生命周期等任务分类，引入智能体原语并展示其在多小区O-RAN模拟中提升性能，还讨论了安全等挑战。
摘要：
开放式RAN（O-RAN）跨非实时RIC、近实时RIC和分布式单元公开了丰富的控制和遥测接口，但也使得以安全且可审计的方式运营多租户、多目标RAN变得更加困难。同时，具有明确规划、工具使用、记忆和自我管理的智能体人工智能系统为构建长期控制回路提供了自然方式。本文研究如何将此类智能体控制器引入O-RAN：回顾O-RAN架构，将智能体控制器与传统ML/RL xApps进行对比，并围绕三个集群组织任务格局：网络切片生命周期、无线电资源管理（RRM）闭环以及跨领域的安全、隐私和合规性。然后引入一小套智能体原语（计划-行动-观察-反思、作为工具使用的技能、记忆和证据以及自我管理门），并在多小区O-RAN模拟中展示，与去除单个原语的传统基线和消融相比，它们如何改善切片生命周期和RRM性能。安全、隐私和合规性被讨论为与标准对齐的部署的架构约束和开放挑战。该框架在三个经典网络切片上平均减少了8.83%的资源使用。
英文摘要：
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.
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。
