Leveraging Large Language Models for Intelligent Control of 6G Integrated TN-NTN With IoT Service

With the advent of sixth generation (6G) Internet of Things (IoT), integrated terrestrial network (TN) and non-terrestrial network (NTN) will play a vital role in enabling new applications and services. However, realizing the potential of 6G integrated TN-NTN requires addressing key challenges like intelligent and optimized control mechanisms for resource management, interference cancellation, and handover management. This paper explores the potential of large language models (LLMs) in intelligent network control for 6G integrated TN-NTN. LLMs can learn complex relationships and patterns from large-scale data, and then be fine-tuned on small labeled datasets, significantly reducing training time and cost. This study examines the main obstacles in the integration of 6G IoT and TN-NTN systems, and further discusses how intelligent control may effectively address those issues. Our suggested approach utilizes LLMs to create efficient anaptive control algorithms that can effectively handle the diverse, ever-changing, and decentralized characteristics of 6G integrated TN-NTN.