# ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception

arXiv: https://arxiv.org/abs/2410.18104 · HTML: https://mlsanity.com/paper/2410.18104/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 11 of its 12 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge](https://mlsanity.com/paper/2310.15051/) (2023)
- [Large Generative AI Models for Telecom: The Next Big Thing?](https://mlsanity.com/paper/2306.10249/) (2023)
- [WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence](https://mlsanity.com/paper/2405.17053/) (2024)
- [When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment](https://mlsanity.com/paper/2401.07764/) (2024)
- [Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems](https://mlsanity.com/paper/2402.01748/) (2024)
- [Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond](https://mlsanity.com/paper/2301.11283/) (2023)
- [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)
- [AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems](https://mlsanity.com/paper/2311.05842/) (2023)
- [DeepSense-V2V: A Vehicle-to-Vehicle Multi-Modal Sensing, Localization, and Communications Dataset](https://mlsanity.com/paper/2406.17908/) (2024)

## Built on by (most cited first, 7 of 7)

- [Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.08826/) (2025)
- [Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook](https://mlsanity.com/paper/2503.18016/) (2025)
- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [Retrieval Augmented Generation with Multi-Modal LLM Framework for Wireless Environments](https://mlsanity.com/paper/2503.07670/) (2025)
- [NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models](https://mlsanity.com/paper/2412.10107/) (2024)
- [ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing](https://mlsanity.com/paper/2506.00576/) (2025)
- [WALoMA: A Multitask Wireless Foundation Model via Adaptive Low-Rank Masked Autoencoders](https://mlsanity.com/paper/2607.25763/) (2026)

## Abstract (arXiv)

Large language models (LLMs) hold significant promise in advancing network management and orchestration in 6G and beyond networks. However, existing LLMs are limited in domain-specific knowledge and their ability to handle multi-modal sensory data, which is critical for real-time situational awareness in dynamic wireless environments. This paper addresses this gap by introducing ENWAR, an ENvironment-aWARe retrieval augmented generation-empowered multi-modal LLM framework. ENWAR seamlessly integrates multi-modal sensory inputs to perceive, interpret, and cognitively process complex wireless environments to provide human-interpretable situational awareness. ENWAR is evaluated on the GPS, LiDAR, and camera modality combinations of DeepSense6G dataset with state-of-the-art LLMs such as Mistral-7b/8x7b and LLaMa3.1-8/70/405b. Compared to general and often superficial environmental descriptions of these vanilla LLMs, ENWAR delivers richer spatial analysis, accurately identifies positions, analyzes obstacles, and assesses line-of-sight between vehicles. Results show that ENWAR achieves key performance indicators of up to 70% relevancy, 55% context recall, 80% correctness, and 86% faithfulness, demonstrating its efficacy in multi-modal perception and interpretation.