RFSensingGPT: a multi-modal RAG-enhanced framework for integrated sensing and communications intelligence in 6G networks
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
We present RFSensingGPT, an integrated framework for radio frequency (RF) sensing that combines technical question-answering, code retrieval, and spectrogram analysis through retrieval-augmented generation (RAG). Our framework addresses the fundamental challenge of applying large language models to RF sensing applications, where specialized domain knowledge is underrepresented in general training corpora. The system leverages a filtered RedPajama dataset containing RF-relevant technical documents, processed through a hybrid retrieval mechanism that combines vector-based similarity search with best match (BM25)-based query fusion. Performance evaluation using document collections ranging from 5K to 80K demonstrates that RAG consistently maintains superior faithfulness across all dataset sizes (0.9033-0.9779 vs 0.8162-0.8506, average improvement of 13.0%) compared to baseline LLM implementations. Our hierarchical chunking approach using MarkdownHeaderTextSplitter achieves optimal precision (0.31-0.32) at lower k-values while maintaining correctness scores of 4.0-5.0. The framework integrates CLIP-based vision models for RF pattern recognition, achieving 93.23% accuracy in radar data analysis tasks. Implementation benchmarks show efficient processing with minimal GPU memory requirements (0.66GB) even at scale. Through a comprehensive evaluation of the embedding models, RFSensingGPT establishes a new benchmark for technical query understanding and RF spectrogram analysis in the emerging field of integrated sensing and communications systems for 6G networks.
Date Issued
2025-04-04
Date Acceptance
2025-04-01
Citation
IEEE Transactions on Cognitive Communications and Networking, 2025, 12, pp.298-311
ISSN
2332-7731
Publisher
Institute of Electrical and Electronics Engineers
Start Page
298
End Page
311
Journal / Book Title
IEEE Transactions on Cognitive Communications and Networking
Volume
12
Copyright Statement
Copyright © 2025 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Date Publish Online
2025-04-04