This paper introduces an AI-driven, data-driven methodology for estimating spectrum demand by leveraging both site license and crowdsourced data. The approach uses an enhanced combined proxy, validated against real-world mobile network traffic, to achieve high predictive accuracy, demonstrating its robustness across multiple major Canadian cities for improved spectrum planning.

This paper provides a comprehensive tutorial on enhancing Reinforcement Learning (RL) for wireless networks using Large Language Models (LLMs). It proposes a taxonomy for LLM roles in RL (state perceiver, reward designer, decision-maker, generator) and showcases their application in various wireless scenarios to address classical RL's limitations in generalization, interpretability, and sample efficiency.