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

Understand

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.