LLM-Aided Spectrum-Sharing LEO Satellite Communications

The rapid expansion of Low Earth Orbit (LEO) satellite constellations has brought significant spectrum management challenges, including spectrum scarcity and complex interference issues. Traditional algorithms and prior Artificial Intelligence (AI) methods fail to meet LEO’s demands for managing extreme dynamics, massive scale, and multi-objective optimization. This paper introduces an innovative Large Language Model (LLM) framework for intelligent spectrum sharing and dynamic resource allocation in satellite-terrestrial down-link systems. First, we established a geometric model for satellite-terrestrial down-link communication, and accurately derived the statistical distribution function of satellites within the space enclosed by a specific orbital line by combining the stochastic geometry theory. Under this geometric model, a communication scenario was introduced, and an adaptive modulation transmission mechanism based on orthogonal frequency division multiplexing signals was designed. Then, the system combines the real-time spectrum sensing results with the natural language description of the quality of service of multi-service data using prompt engineering techniques, and delivers the comprehensive information to the LLM for resource allocation and generation of a transmission scheme. Finally, the resource allocation and transmission scheme determined by the LLM is applied to the established communication model, and the system performance is comprehensively evaluated by analyzing indicators such as outage probability, system throughput, and transmission and waiting delays. Primary contributions include novel dynamic service-to-strategy generation, an LLM-centric prompt-driven architecture, and a new paradigm that positions the LLM as an intelligent “spectrum orchestration brain” for complex global LEO resource management. Collectively, these advancements enhance spectrum utilization intelligence, adaptability, and efficiency, offering a transformative approach to overcome the limitations of prior methods in demanding LEO environments.