Empowering Future Spectrum Management and Regulation With Large Language Models

Spectrum management and regulation are becoming more complex due to rapid technological advancements, increasing demand for spectrum, and the presence of diverse stakeholders with conflicting interests. In response, governments worldwide are increasingly interested in leveraging advanced technologies, such as Artificial Intelligence (AI), to enhance efficiency and optimize policy outcomes. This paper explores the application of large language models (LLMs), a subset of generative AI, to streamline tasks and improve decision-making in spectrum management and regulation. It examines the various roles of LLMs in this field and addresses associated challenges. Through empirical case studies and experimental findings, the paper demonstrates how LLMs can profoundly transform spectrum management and regulation practices. The study also offers insights into effectively integrating AI into regulatory frameworks, providing practical lessons and best practices for governmental AI initiatives.