用大模型提升无线网络智能控制器协作效率
LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN
- 分层架构：大模型做全局策略指导，强化学习负责实时执行
- 在集成接入回传网络中验证，提升控制器协同决策能力
- 适合研究AI与通信融合、5G/6G网络智能化的从业者
尽管大语言模型（LLMs）和机器学习（ML）技术在开放无线接入网（O-RAN）中取得进展，但仍面临智能控制器间协作不足、计算负担重影响实时决策、缺乏领域特定微调等挑战。为此，本文提出一种大模型赋能的分层无线接入网智能控制器（LLM-hRIC）框架，以增强O-RAN中控制器间的协同。非实时控制器（non-RT RIC）作为全局策略引导者，利用全网信息为近实时控制器（near-RT RIC）提供战略建议；近实时控制器作为执行者，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（IAB）网络场景下评估了LLM-hRIC框架的可行性与性能，并讨论其在O-RAN中的开放挑战。
原文摘要 · Abstract (English)
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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