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

Muhammad Ahmed Mohsin, Ahsan Bilal, Sagnik Bhattacharya, John M. Cioffi

TL;DR

This work integrates retrieval-augmented generation with multimodal sensing to optimize wireless environments. By converting sensor data (images, GPS, LiDAR) into a unified textual and vector-based knowledge base via GPT-4o, YOLO, and ChromaDB, it enables LLM-driven decisions with real-time latency. Evaluations on GPT-4 and Gemini show improvements in relevancy, faithfulness, completeness, similarity, and accuracy by 8%, 8%, 10%, 7%, and 12%, respectively, while maintaining efficiency suitable for ISAC-like setups. The framework demonstrates practical potential for context-aware wireless optimization in complex 6G scenarios, with future work aimed at broader ISAC integration and real-time deployment.

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.

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

TL;DR

This work integrates retrieval-augmented generation with multimodal sensing to optimize wireless environments. By converting sensor data (images, GPS, LiDAR) into a unified textual and vector-based knowledge base via GPT-4o, YOLO, and ChromaDB, it enables LLM-driven decisions with real-time latency. Evaluations on GPT-4 and Gemini show improvements in relevancy, faithfulness, completeness, similarity, and accuracy by 8%, 8%, 10%, 7%, and 12%, respectively, while maintaining efficiency suitable for ISAC-like setups. The framework demonstrates practical potential for context-aware wireless optimization in complex 6G scenarios, with future work aimed at broader ISAC integration and real-time deployment.

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.

Paper Structure

This paper contains 24 sections, 13 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Deepsense 6G Dataset
  3. Environment Description
  4. Data Acquisition
  5. Proposed Framework: Multi-Modal RAG for Wireless Environment Perception
  6. Multi Modal Data Preprocessing
  7. Text to image description
  8. GPS calculations
  9. Object detection using YOLO
  10. Lidar Images
  11. DeepSense 6G data retrieval
  12. Knowledge Base Creation with ChromaDB
  13. Ranking the results and LLM integration
  14. Wireless Communications Domain-Specific Prompt Engineering for Multi-Modal Input
  15. Structured Information and result ranking
  16. ...and 9 more sections

Figures (4)

  • Figure 1: Work flow for vector database creation for RAG-based LLM wireless environment perception
  • Figure 2: A detailed response comparison for RAG-based LLM vs. Vanilla LLM [baseline]
  • Figure 3: (a) Relevancy: RAG-based LLMs [Cam, YOLO, GPS]; (b) Response time: Gen AI models; (c) Accuracy & fluency: RAG-based LLMs [0-4].
  • Figure 4: (a) Correctness, (b) Faithfulness, (c) Similarity: RAG-based vs. baseline LLMs [0-1].