RFSensingGPT: A Multi-Modal RAG-Enhanced Framework for Integrated Sensing and Communications Intelligence in 6G Networks

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.