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.

中文翻译：

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利用大型语言模型实现 6G 集成 TN-NTN 与物联网服务的智能控制


随着第六代（6G）物联网（IoT）的出现，综合地面网络（TN）和非地面网络（NTN）将在实现新应用和服务方面发挥至关重要的作用。然而，要实现 6G 集成 TN-NTN 的潜力，需要解决关键挑战，例如资源管理、干扰消除和切换管理的智能和优化控制机制。本文探讨了大型语言模型的潜力（LLMs ）在6G集成TN-NTN的智能网络控制中。LLMs可以从大规模数据中学习复杂的关系和模式，然后在小型标记数据集上进行微调，从而显着减少训练时间和成本。本研究探讨了 6G IoT 与 TN-NTN 系统集成的主要障碍，并进一步讨论智能控制如何有效解决这些问题。我们建议的方法利用LLMs创建高效的自适应控制算法，能够有效处理6G集成TN-NTN的多样化、不断变化和分散的特性。