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

TL;DR

ENWAR is a novel multi-modal LLM framework that enhances wireless environment perception by integrating sensory data for real-time situational awareness, surpassing existing models in accuracy and interpretability.

Contribution

This paper introduces ENWAR, a multi-modal LLM framework that effectively combines sensory inputs for improved wireless environment understanding, a significant advancement over prior domain-specific models.

Findings

Achieves up to 70% relevancy in environment descriptions

Attains 55% in context recall for environment understanding

Reaches 80% correctness and 86% faithfulness in perception tasks

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.