LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN

Abstract--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 (R AN) intelligent controllers (RICs), high computational deman ds hindering real-time decisions, and the lack of domain-specific fine-tuning. Therefore, this article introduces the LLM-empowe red hierarchical RIC (LLM-hRIC) framework to improve the collaboration between RICs in O-RAN. 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, disc uss the open challenges of the LLM-hRIC framework for O-RAN. The open radio access network (O-RAN) has recently gained significant attention for its ability to prompt inter op-erability and flexibility.