Despite recent advances in applying large language models (LLMs) and machine learning (ML) techniques to open radio access network (O-RAN), critical challenges remain, such as insufficient cooperation between radio access network (RAN) intelligent controllers (RICs), high computational demands hindering real-time decisions, and the lack of domain-specific finetuning. Therefore, this article introduces the LLM-empowered hierarchical RIC (LLM-hRIC) framework to improve the collaboration between RICs in O-RAN. The LLM-empowered non-real-time RIC (non-RT RIC) acts as a guider, offering a strategic guidance to the near-real-time RIC (near-RT RIC) using global network information. The RL-empowered near-RT RIC acts as an implementer, combining this guidance with local real-time data to make near-RT decisions. We evaluate the feasibility and performance of the LLM-hRIC framework in an integrated access and backhaul (IAB) network setting, and finally, discuss the open challenges of the LLM-hRIC framework for O-RAN.

翻译：尽管近期在将大语言模型（LLM）与机器学习（ML）技术应用于开放无线接入网络（O-RAN）方面取得了进展，但仍存在若干关键挑战，例如无线接入网智能控制器（RIC）之间协作不足、高计算需求阻碍实时决策，以及缺乏领域特定的微调。为此，本文提出了LLM赋能的层次化RIC（LLM-hRIC）框架，以提升O-RAN中RIC间的协作能力。其中，LLM赋能的非实时RIC（non-RT RIC）充当引导者角色，利用全局网络信息为近实时RIC（near-RT RIC）提供策略性指导；而强化学习赋能的近实时RIC则作为执行者，结合该指导与本地实时数据做出近实时决策。我们在集成接入与回传网络场景下评估了LLM-hRIC框架的可行性与性能，并最后讨论了该框架在O-RAN应用中所面临的开放挑战。