The transition to 6G networks promises unprecedented advancements in wireless communication, with increased data rates, ultra-low latency, and enhanced capacity. However, the complexity of managing and optimizing these next-generation networks presents significant challenges. The advent of large language models (LLMs) has revolutionized various domains by leveraging their sophisticated natural language understanding capabilities. However, the practical application of LLMs in wireless network orchestration and management remains largely unexplored. Existing literature predominantly offers visionary perspectives without concrete implementations, leaving a significant gap in the field. To address this gap, this paper presents NETORCHLLM, a wireless NETwork ORCHestrator LLM framework that uses LLMs to seamlessly orchestrate diverse wireless-specific models from wireless communication communities using their language understanding and generation capabilities. A comprehensive framework is introduced, demonstrating the practical viability of our approach and showcasing how LLMs can be effectively harnessed to optimize dense network operations, manage dynamic environments, and improve overall network performance. NETORCHLLM bridges the theoretical aspirations of prior research with practical, actionable solutions, paving the way for future advancements in integrating generative AI technologies within the wireless communications sector.

翻译：向6G网络的过渡预示着无线通信领域将迎来前所未有的进步，包括更高的数据速率、超低延迟和增强的容量。然而，管理和优化这些下一代网络的复杂性带来了重大挑战。大语言模型（LLMs）凭借其复杂的自然语言理解能力，已在多个领域引发革命性变革。然而，LLMs在无线网络编排与管理中的实际应用仍很大程度上未被探索。现有文献主要提供愿景性观点而缺乏具体实现，导致该领域存在显著空白。为填补这一空白，本文提出了NETORCHLLM——一种无线网络编排大语言模型框架，该框架利用LLMs的语言理解与生成能力，无缝编排来自无线通信社区的各种无线专用模型。本文引入了一个综合性框架，证明了我们方法的实际可行性，并展示了如何有效利用LLMs来优化密集网络运营、管理动态环境并提升整体网络性能。NETORCHLLM将先前研究的理论愿景与切实可行的解决方案相连接，为生成式人工智能技术在无线通信领域的融合应用铺平了道路。