# Retrieval Augmented Generation with Multi-Modal LLM Framework for Wireless Environments

arXiv: https://arxiv.org/abs/2503.07670 · HTML: https://mlsanity.com/paper/2503.07670/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 14 of its 18 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 14 of 14)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation](https://mlsanity.com/paper/2404.05970/) (2024)
- [HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction](https://mlsanity.com/paper/2408.04948/) (2024)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [The Importance of Directional Feedback for LLM-based Optimizers](https://mlsanity.com/paper/2405.16434/) (2024)
- [At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence](https://mlsanity.com/paper/2402.18587/) (2024)
- [LLMs for XAI: Future Directions for Explaining Explanations](https://mlsanity.com/paper/2405.06064/) (2024)
- [GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning](https://mlsanity.com/paper/2402.16631/) (2024)
- [Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP Standards](https://mlsanity.com/paper/2408.11775/) (2024)
- [ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception](https://mlsanity.com/paper/2410.18104/) (2024)
- [Deep Reinforcement Learning Optimized Intelligent Resource Allocation in Active RIS-Integrated TN-NTN Networks](https://mlsanity.com/paper/2501.06482/) (2025)

## Built on by (most cited first, 5 of 5)

- [LLMs for Explainable AI: A Comprehensive Survey](https://mlsanity.com/paper/2504.00125/) (2025)
- [Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey](https://mlsanity.com/paper/2504.14520/) (2025)
- [GOBench: Benchmarking Geometric Optics Generation and Understanding of MLLMs](https://mlsanity.com/paper/2506.00991/) (2025)
- [OnRL-RAG: Real-Time Personalized Mental Health Dialogue System](https://mlsanity.com/paper/2504.02894/) (2025)
- [Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks](https://mlsanity.com/paper/2508.19495/) (2025)

## Abstract (arXiv)

Future wireless networks aim to deliver high data rates and lower power consumption while ensuring seamless connectivity, necessitating robust optimization. Large language models (LLMs) have been deployed for generalized optimization scenarios. To take advantage of generative AI (GAI) models, we propose retrieval augmented generation (RAG) for multi-sensor wireless environment perception. Utilizing domain-specific prompt engineering, we apply RAG to efficiently harness multimodal data inputs from sensors in a wireless environment. Key pre-processing pipelines including image-to-text conversion, object detection, and distance calculations for multimodal RAG input from multi-sensor data are proposed to obtain a unified vector database crucial for optimizing LLMs in global wireless tasks. Our evaluation, conducted with OpenAI's GPT and Google's Gemini models, demonstrates an 8%, 8%, 10%, 7%, and 12% improvement in relevancy, faithfulness, completeness, similarity, and accuracy, respectively, compared to conventional LLM-based designs. Furthermore, our RAG-based LLM framework with vectorized databases is computationally efficient, providing real-time convergence under latency constraints.