AGI and LLM-Driven Spectrum Intelligence in Future Wireless Networks

Artificial General Intelligence (AGI) and Large Language Models (LLMs) are gaining attention for their transformative potential across various fields. While LLMs have significantly advanced Natural Language Processing (NLP), they face challenges in reasoning, adaptability, and bias. AGI, with its human-like cognitive functions, offers a promising solution by enhancing the flexibility and context-awareness of LLMs. This paper explores the integration of AGI with LLMs to address complex, dynamic problems, focusing on advancements in Cognitive Radio (CR) and Spectrum Intelligence (SI) technologies. Spectrum sensing, a cornerstone of CR and SI, is critical for identifying underutilized frequency bands and mitigating interference. Traditional methods often struggle in dynamic environments due to their reliance on static models. By combining AGI’s adaptive decision-making with LLMs’ context-aware understanding, the integrated system can enhance the accuracy and efficiency of spectrum sensing. This integration enables better processing of diverse data, prediction of spectrum usage, and dynamic adaptation to changing conditions, paving the way for intelligent spectrum management. As the demand for efficient communication grows with the proliferation of connected devices, AGI-augmented LLMs offer scalable, context-aware solutions to modern communication challenges. AGI with LLMs has the potential to transform spectrum sensing and management into a more adaptive, efficient paradigm, ensuring the performance of next-generation wireless networks.