# LLM-Based Intent Processing and Network Optimization Using Attention-Based Hierarchical Reinforcement Learning

arXiv: https://arxiv.org/abs/2406.06059 · HTML: https://mlsanity.com/paper/2406.06059/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2024.

## Built on by (most cited first, 14 of 14)

- [ORAN-Bench-13K: An Open Source Benchmark for Assessing LLMs in Open Radio Access Networks](https://mlsanity.com/paper/2407.06245/) (2024)
- [LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach](https://mlsanity.com/paper/2505.12247/) (2025)
- [ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing](https://mlsanity.com/paper/2506.00576/) (2025)
- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicing](https://mlsanity.com/paper/2506.00574/) (2025)
- [Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows](https://mlsanity.com/paper/2512.20640/) (2025)
- [Online Learning for Autonomous Management of Intent-based 6G Networks](https://mlsanity.com/paper/2407.17767/) (2024)
- [Generative AI for Intent-Driven Network Management in 6G RAN: A Case Study on the Mamba Model](https://mlsanity.com/paper/2508.06616/) (2025)
- [Chat-Driven Optimal Management for Virtual Network Services](https://mlsanity.com/paper/2512.24614/) (2025)
- [Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization](https://mlsanity.com/paper/2602.13210/) (2026)
- [STAR: Failure-Aware Markovian Routing for Multi-Agent Spatiotemporal Reasoning](https://mlsanity.com/paper/2605.10057/) (2026)
- [AGORA: Agentic Green Orchestration Architecture for Beyond 5G Networks](https://mlsanity.com/paper/2602.13290/) (2026)
- [Smart Adaptive Computing Across the Continuum: LLMs in IoT-Edge-Cloud Resource Management](https://mlsanity.com/paper/2609.09348/) (2026)
- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

Intent-based network automation is a promising tool to enable easier network management however certain challenges need to be effectively addressed. These are: 1) processing intents, i.e., identification of logic and necessary parameters to fulfill an intent, 2) validating an intent to align it with current network status, and 3) satisfying intents via network optimizing functions like xApps and rApps in O-RAN. This paper addresses these points via a three-fold strategy to introduce intent-based automation for O-RAN. First, intents are processed via a lightweight Large Language Model (LLM). Secondly, once an intent is processed, it is validated against future incoming traffic volume profiles (high or low). Finally, a series of network optimization applications (rApps and xApps) have been developed. With their machine learning-based functionalities, they can improve certain key performance indicators such as throughput, delay, and energy efficiency. In this final stage, using an attention-based hierarchical reinforcement learning algorithm, these applications are optimally initiated to satisfy the intent of an operator. Our simulations show that the proposed method can achieve at least 12% increase in throughput, 17.1% increase in energy efficiency, and 26.5% decrease in network delay compared to the baseline algorithms.