RadioLLM introduces a novel framework that brings large language model capabilities to cognitive radio technology, addressing the critical challenge of spectrum resource scarcity.

Key innovations:

• Hybrid prompt and token reprogramming approach for radio signal processing
• Enhanced scalability across diverse radio network scenarios
• Transformation of task-specific models into a unified, adaptable framework
• Improved efficiency in signal classification and spectrum allocation

This research represents a significant advancement for communications engineering by creating more intelligent, adaptive radio systems capable of operating in complex, resource-constrained environments.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings