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

arXiv: https://arxiv.org/abs/2504.18062 · HTML: https://mlsanity.com/paper/2504.18062/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 25 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control](https://mlsanity.com/paper/2408.00214/) (2024)
- [Toward Next Generation Open Radio Access Network--What O-RAN Can and Cannot Do!](https://mlsanity.com/paper/2111.13754/) (2021)
- [Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning](https://mlsanity.com/paper/2202.06439/) (2022)
- [Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings](https://mlsanity.com/paper/2405.13356/) (2024)
- [Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework](https://mlsanity.com/paper/2302.02711/) (2023)
- [Enhancing Energy Efficiency in O-RAN Through Intelligent xApps Deployment](https://mlsanity.com/paper/2405.10116/) (2024)
- [Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization](https://mlsanity.com/paper/2408.03631/) (2024)

## Built on by (most cited first, 6 of 6)

- [Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning](https://mlsanity.com/paper/2511.02748/) (2025)
- [Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm](https://mlsanity.com/paper/2512.04405/) (2025)
- [The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network](https://mlsanity.com/paper/2509.10478/) (2025)
- [MobiLLM: An Agentic AI Framework for Closed-Loop Threat Mitigation in 6G Open RANs](https://mlsanity.com/paper/2509.21634/) (2025)
- [6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence](https://mlsanity.com/paper/2605.01546/) (2026)
- [LLM-Powered Agentic AI for 5G/6G Networks: A Tutorial and Survey on Architectures, Protocols, and Standardization](https://mlsanity.com/paper/2607.16066/) (2026)

## Abstract (arXiv)

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.