# Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G

arXiv: https://arxiv.org/abs/2505.01841 · HTML: https://mlsanity.com/paper/2505.01841/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2025.

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

- [Edge Learning via Federated Split Decision Transformers for Metaverse Resource Allocation](https://mlsanity.com/paper/2602.16174/) (2026)
- [Agentic AI for Mobile Network RAN Management and Optimization](https://mlsanity.com/paper/2511.02532/) (2025)
- [Generative AI for Intent-Driven Network Management in 6G RAN: A Case Study on the Mamba Model](https://mlsanity.com/paper/2508.06616/) (2025)
- [Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers](https://mlsanity.com/paper/2606.04328/) (2026)
- [AGORA: Agentic Green Orchestration Architecture for Beyond 5G Networks](https://mlsanity.com/paper/2602.13290/) (2026)
- [WiSDoM: Wireless Sparse Decision Transformer with Mixture-of-Experts for Multi-Task Mobile Network Optimization](https://mlsanity.com/paper/2609.00284/) (2026)

## Abstract (arXiv)

Intent-driven network management is critical for managing the complexity of 5G and 6G networks. It enables adaptive, on-demand management of the network based on the objectives of the network operators. In this paper, we propose an innovative three-step framework for intent-driven network management based on Generative AI (GenAI) algorithms. First, we fine-tune a Large Language Model (LLM) on a custom dataset using a Quantized Low-Rank Adapter (QLoRA) to enable memory-efficient intent processing within limited computational resources. A Retrieval Augmented Generation (RAG) module is included to support dynamic decision-making. Second, we utilize a transformer architecture for time series forecasting to predict key parameters, such as power consumption, traffic load, and packet drop rate, to facilitate intent validation proactively. Lastly, we introduce a Hierarchical Decision Transformer with Goal Awareness (HDTGA) to optimize the selection and orchestration of network applications and hence, optimize the network. Our intent guidance and processing approach improves BERTScore by 6% and the semantic similarity score by 9% compared to the base LLM model. Again, the proposed predictive intent validation approach can successfully rule out the performance-degrading intents with an average of 88% accuracy. Finally, compared to the baselines, the proposed HDTGA algorithm increases throughput at least by 19.3%, reduces delay by 48.5%, and boosts energy efficiency by 54.9%.