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

Ahmad M. Nazar, Abdulkadir Celik, Mohamed Y. Selim, Asmaa Abdallah, Daji Qiao, Ahmed M. Eltawil

TL;DR

ENWAR is introduced, an ENvironment-aWARe retrieval augmented generation-empowered multi-modal LLM framework that seamlessly integrates multi-modal sensory inputs to perceive, interpret, and cognitively process complex wireless environments to provide human-interpretable situational awareness.

Abstract

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.

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

TL;DR

ENWAR is introduced, an ENvironment-aWARe retrieval augmented generation-empowered multi-modal LLM framework that seamlessly integrates multi-modal sensory inputs to perceive, interpret, and cognitively process complex wireless environments to provide human-interpretable situational awareness.

Abstract

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.

Paper Structure

This paper contains 28 sections, 3 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. An Overview of Enwar Framework
  3. Enwar Setup Breakdown and A Case Study
  4. KPI Evaluation of State-of-the-Art LLMs on Modality Combinations
  5. Conclusion and Future Directions

Figures (3)

  • Figure 1: Enwar workflows: multi-modal RAG formation (Steps A-C); and prompt interpretation, knowledge retrieval, and response generation (Steps 1-5).
  • Figure 2: Illustration of the case study scene with raw data, extracted information, generated prompt, and responses from Vanilla Llama and Enwar.
  • Figure 3: KPI comparison of LLM across modality combinations: the first and second rows present absolute KPI [%] and KPI normalized per billion parameters of each LLM [%pb], respectively.