STR-Agent: An LLM-Driven Agent for QoS-Aware Routing in LEO Satellite Networks
STR-Agent, an LLM-driven framework, translates natural-language service requests into adaptive QoS-aware routing for LEO satellite networks, cutting end-to-end delay by up to 60%.
STR-Agent is an LLM-driven framework for QoS-aware routing in LEO satellite networks that unifies intent perception, tool-based execution, experience accumulation, and reflection-based policy adaptation in a single agent architecture. A Perception Module converts natural-language requests into structured routing semantics, while a Reflection Module adjusts service-to-routing-policy mappings based on real-time congestion and historical outcomes. In Walker-Delta constellation simulations it reduces end-to-end delay by up to 60% versus DQ-Dijkstra, raises intent-understanding accuracy from 45.4% to 92.45% after supervised fine-tuning, and its Reflection Module further cuts delay by 120 ms at 600 Mbps.
- Unifies intent perception, tool-based execution, experience accumulation, and reflection-based policy adaptation in one agent architecture.
- Domain-specific supervised fine-tuning raises intent-understanding accuracy from 45.4% to 92.45%.
- Reduces end-to-end delay by up to 60% versus DQ-Dijkstra in Walker-Delta constellation simulations.
- Reflection Module cuts delay by 120 ms at 600 Mbps using real-time congestion signals.
From the original article
1 min read at arxiv.orgLEO 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.
By Bowen Lu · arxiv.org
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