LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN
This addresses challenges in O-RAN for telecommunications by enhancing RIC cooperation, though it appears incremental as it builds on existing LLM and ML techniques.
The paper tackles the problem of insufficient cooperation and high computational demands in O-RAN by introducing the LLM-hRIC framework, which uses an LLM-empowered non-real-time RIC to guide an RL-empowered near-real-time RIC, improving collaboration in an integrated access and backhaul network setting.
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.