STR-Agent：一种面向LEO卫星网络QoS感知路由的LLM驱动智能体
STR-Agent: An LLM-Driven Agent for QoS-Aware Routing in LEO Satellite Networks
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中文总结 AI 辅助
针对LEO卫星网络QoS路由难以处理自然语言服务请求的问题，提出LLM驱动的STR-Agent框架，通过意图感知、工具执行、经验积累和反思自适应统一架构，显著降低延迟并提升意图理解准确率。
中文摘要 AI 辅助
LEO卫星网络具有动态拓扑、时变链路和多样化服务需求的特点，这使得传统路由方案难以支持细粒度的服务质量（QoS）保障。现有研究主要基于预定义目标在网络状态上优化路由，但很少解决将非结构化自然语言服务请求转化为自适应路由决策这一实际挑战。为弥补这一差距，我们提出了STR-Agent，一种用于LEO卫星网络中QoS感知路由的LLM驱动框架。STR-Agent的关键创新在于将意图感知、基于工具的执行、经验积累和基于反思的策略自适应统一在单一智能体架构中。具体而言，感知模块将自然语言请求转换为结构化的路由语义，而反思模块根据实时拥塞状况和历史路由结果动态调整服务到路由策略的映射，而非依赖固定的路由目标。此外，我们开发了一个专门的感知模型，并构建了一个面向LEO服务理解的领域特定监督微调数据集。在Walker-Delta星座中的仿真结果表明，STR-Agent显著优于传统基线：与DQ-Dijkstra相比，端到端延迟最多降低60%；监督微调后平均意图理解准确率从45.4%提升至92.45%；反思模块在600 Mbps下进一步将延迟降低120毫秒。这些结果展示了LLM驱动的智能体架构在未来LEO卫星网络中实现服务感知和自适应QoS路由的潜力。
英文摘要
LEO satellite networks feature dynamic topologies, time-varying links, and diverse service requirements, which make conventional routing schemes difficult to support fine-grained quality-of-service (QoS) provisioning. Existing studies mainly optimize routing over network states with predefined objectives, but rarely address the practical challenge of translating unstructured natural-language service requests into adaptive routing decisions. To bridge this gap, we propose STR-Agent, an LLM-driven framework for QoS-aware routing in LEO satellite networks. The key innovation of STR-Agent lies in unifying intent perception, tool-based execution, experience accumulation, and reflection-based policy adaptation within a single agent architecture. Specifically, the Perception Module converts natural-language requests into structured routing semantics, while the Reflection Module dynamically adjusts the service-to-routing-policy mapping according to real-time congestion conditions and historical routing outcomes, rather than relying on a fixed routing objective. In addition, we develop a specialized perception model, and construct a domain-specific supervised fine-tuning dataset for LEO service understanding. Simulation results in a Walker-Delta constellation show that STR-Agent significantly outperforms conventional baselines: it reduces end-to-end delay by up to 60% compared with DQ-Dijkstra, improves average intent-understanding accuracy from 45.4% to 92.45% after supervised fine-tuning, and the Reflection Module further reduces the delay by 120 ms at 600 Mbps. These results demonstrate the potential of LLM-driven agent architectures to enable service-aware and adaptive QoS routing in future LEO satellite networks.
发表机构
- Beijing University of Posts and Telecommunications（北京邮电大学）
机构由 AI 辅助整理，请以论文原文为准。
