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

• URL: http://arxiv.org/abs/2504.18062v2
• Date: Tue, 20 May 2025 07:43:35 GMT
• Title: LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN
• Authors: Lingyan Bao, Sinwoong Yun, Jemin Lee, Tony Q. S. Quek,
• Abstract summary: This article introduces the LLM-empowered hierarchical RIC (LLM-hRIC) framework to improve the collaboration between RICs in radio access network (O-RAN)<n>The framework offers a strategic guidance to the near-real-time RIC (non-RT RIC) using global network information.<n>The RL-empowered near-RT RIC acts as an implementer, combining this guidance with local real-time data to make near-RT decisions.
• Score: 56.94324843095396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (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.

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