发表机构
Institute for Intelligent Networked Systems, Northeastern University（东北大学智能网联系统研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出首个基于LLM智能体的自主RDZ系统A-RDZ，结合确定性策略门控与近实时反射，在FR3 O-RAN测试平台上验证了与FSS共存场景下的违规检测与缓解能力。
AI 中文摘要
无线电动态区域（RDZ）允许无线实验在传统频谱法规之外运行，同时持续保障现有用户受到保护。现有的RDZ原型通过手工规则和预定义工作流以程序化方式自动化此任务，当实验遇到硬件损伤、用户工作流和设备，或设计时未预料到的干扰机制时，这些原型会变得脆弱。本文介绍了智能体RDZ（A-RDZ），据我们所知，这是首个RDZ实现，其中智能体使用大型语言模型（LLMs）来执行频谱管理、实验管理、策略解释和区域编排。该架构基于GENESIS智能体框架，将自主推理与确定性策略门控和近实时（near-RT）反射相结合，使得智能体能够改善结果但绝不会削弱区域的保护保障。我们在一个硬件在环的Frequency Range 3（FR3）（7.125-24.25 GHz）开放无线接入网（O-RAN）测试平台上验证了A-RDZ，其中5G新空口（NR）实验与模拟的固定卫星业务（FSS）地球站现有用户共存。在一个端到端用例中，监控智能体从实时频谱证据中检测到发射违规，编排器选择一种缓解措施，在保持实验运行的同时恢复干扰预算，并通过O-RAN控制平面应用和验证该操作。我们报告了从检测到缓解的延迟分解，并讨论了智能体操作的实践限制，包括非确定性推理和从决策到行动的转换。
英文摘要
Radio Dynamic Zones (RDZs) allow wireless experiments to operate outside conventional spectrum regulations while continuously guaranteeing protection for incumbent users. Existing RDZ prototypes automate this task procedurally, through handcrafted rules and predefined workflows, and become brittle when experiments encounter hardware impairments, user workflows and devices, or interference mechanisms not anticipated at design time. This paper introduces the agentic RDZ (A-RDZ), which, to the best of our knowledge, is the first RDZ realization in which agents use Large Language Models (LLMs) to perform spectrum management, experiment management, policy interpretation, and zone orchestration. Built on the GENESIS agentic framework, the architecture pairs autonomous reasoning with a deterministic policy gate and near-real-time (near-RT) reflexes, so that agents can improve outcomes but never weaken the zone's protection guarantee. We validate the A-RDZ on a hardware-in-the-loop Frequency Range 3 (FR3) (7.125-24.25 GHz) Open Radio Access Network (O-RAN) testbed in which a 5G New Radio (NR) experiment coexists with an emulated Fixed Satellite Service (FSS) earth-station incumbent. In an end-to-end use case, the monitoring agent detects an emission violation from live spectrum evidence, the orchestrator selects a mitigation that restores the interference budget while keeping the experiment running, and the action is applied and verified through the O-RAN control plane. We report the detection-to-mitigation latency decomposition and discuss the practical limits of agentic operation, including non-deterministic reasoning and decision-to-action translation.
