From Commands to Cognition: An LLM-Driven Satellite Agent for Autonomous Spectrum Sensing

A low earth orbit (LEO) satellite traverses multiple distinct electromagnetic environments within every 94-minute orbit, from signal-dense urban corridors that demand high-precision detection to near-silent oceans where lighter sensing conserves scarce battery. Existing on-board schedulers can only switch among pre-configured sensing modules, and cannot reason about task feasibility, resource implications, or ambiguous mission intents issued from the ground. This limitation is increasingly critical for cognitive radio IoT (CR-IoT), where timely and wide-area spectrum awareness underpins dynamic spectrum access and interference management. In this paper, we propose LEOScA, an intent-aware LLM-driven orchestration framework for LEO satellite spectrum monitoring. The framework employs an LLM as a high-level reasoning engine to transform natural language monitoring intents into structured execution strategies. To further mitigate inherent limitations of LLM in structured decision-making scenarios, we design a prompt-chain-based hierarchical strategy scheduling (PC-HSS) algorithm that decomposes on-board decision-making into three coordinated modules and invokes the LLM only upon the arrival of a new task. Experimental results show that LEOScA achieves 100% feasible-task completion rate, compared with 88% for the best static baseline, while reducing cumulative energy consumption by 9.7% under mixed-instruction scenarios. These results demonstrate the feasibility of autonomous LLM-driven spectrum sensing on resource-constrained satellite platforms, thereby enabling self-managing CR-IoT infrastructure in space.