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

2025-03-09

AI Summary

To address the joint optimization challenges of high data rates, low power consumption, and seamless connectivity in future wireless networks, this paper proposes a wireless-environment-oriented multimodal Retrieval-Augmented Generation (RAG) framework. The method introduces a novel unified vector database construction technique that fuses heterogeneous sensing data—including images, distance measurements, and object detection outputs—coupled with domain-specific prompt engineering and a lightweight preprocessing pipeline to enable real-time, high-accuracy wireless environment perception and decision-making. Compared to conventional large language model (LLM)-based approaches, the framework achieves improvements of 8%, 8%, 10%, 7%, and 12% in relevance, faithfulness, completeness, similarity, and accuracy, respectively. These gains significantly enhance end-to-end wireless task performance while supporting low-latency, real-time convergence.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Optimize wireless networks for high data rates and low power consumption.
• Enhance multi-sensor wireless environment perception using generative AI.
• Improve LLM performance in wireless tasks with retrieval augmented generation.

Innovation

• Retrieval augmented generation for multi-sensor data
• Domain-specific prompt engineering for multimodal inputs
• Vectorized databases for real-time LLM optimization