智能体射频智能：基于设备端基础模型的多时间尺度6G感知与推理
Agentic RF Intelligence: Multi-Timescale 6G Sensing and Reasoning with On-Device Foundation Models
- Ghent University（根特大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出基于双环架构的智能体射频智能，将毫秒级无线感知与秒级LLM推理解耦，并在NVIDIA Jetson Thor上验证可行性，为6G边缘感知与推理提供新范式。
AI中文摘要：
未来6G系统与物理人工智能面临一个共同的核心挑战：在严格延迟、计算和能源约束下，于快速变化的物理环境中，在网络边缘基础设施上结合感知、推理和行动。应对这一挑战需要多时间尺度的智能，将跟踪无线现象（毫秒级）的快速感知与指导感知并在数秒内调整网络策略和资源的较慢推理相结合。本文提出了一种基于双环架构的智能体射频智能愿景，该架构将快速、本地自主的无线感知（例如检测频谱动态、干扰和信号源）与较慢的智能体推理和编排解耦。我们提供了一个设备端原型，其中整个技术栈运行在连接到B200 Mini USRP的单个NVIDIA Jetson Thor上。快速环路使用信号处理工具和无线物理层基础模型（WPFM）处理IQ样本，而本地大语言模型（LLM）异步解释射频事件、调用工具并引导感知。我们的实验证实了时间尺度的分离，WPFM以毫秒级延迟运行，而LLM交互需要数秒到数分钟。在智能体推理期间，快速环路保持运行，为这种解耦架构的可行性提供了初步证据。最后，我们概述了向记忆驱动的自我改进、世界模型、网络控制和多智能体操作的扩展方向。
英文摘要：
Future 6G systems share a central challenge with Physical AI: combining sensing, reasoning, and action on network-edge infrastructure in rapidly changing physical environments under strict latency, compute, and energy constraints. Addressing this challenge requires multi-timescale intelligence, combining fast perception that tracks wireless phenomena within milliseconds with slower reasoning that directs sensing and adapts network policies and resources over seconds. In this paper, we present a vision for Agentic RF Intelligence based on a dual-loop architecture that decouples fast, locally autonomous wireless sensing (e.g., detecting spectrum dynamics, interference, and signal sources) from slower agentic reasoning and orchestration. We provide an on-device prototype in which the full stack runs on a single NVIDIA Jetson Thor connected to a B200 Mini USRP. The fast loop processes IQ samples using signal-processing tools and wireless physical-layer foundation models (WPFM), while a local Large Language Model (LLM) asynchronously interprets RF events, invokes tools and steers sensing. Our experiments confirm the separation in timescales, as WPFMs operate at millisecond latency, while LLM interactions take seconds to minutes. The fast loop remains operational during agentic reasoning, providing initial evidence for the feasibility of this decoupled architecture. We conclude by outlining extensions toward memory-driven self-improvement, world models, network control, and multi-agent operation.
