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[2602.14833] RF-GPT: Teaching AI to See the Wireless World
Summary
RF-GPT introduces a novel radio-frequency language model that bridges the gap between RF signal processing and high-level reasoning using multimodal LLMs.
Why It Matters
This research addresses a significant gap in AI capabilities by integrating radio-frequency signal understanding into language models, enhancing applications in telecommunications and wireless technology. As wireless systems become increasingly complex, the ability to analyze and interpret RF signals through AI is crucial for innovation and efficiency in the industry.
Key Takeaways
- RF-GPT utilizes visual encoders to process RF spectrograms, enhancing AI's understanding of wireless signals.
- The model achieves strong multi-task performance across various wireless technology benchmarks.
- Training is performed using a synthetic RF corpus, eliminating the need for manual labeling.
- This approach highlights the potential for integrating RF data into general-purpose AI systems.
- RF-GPT represents a significant advancement in bridging RF perception with high-level reasoning.
Electrical Engineering and Systems Science > Signal Processing arXiv:2602.14833 (eess) [Submitted on 16 Feb 2026] Title:RF-GPT: Teaching AI to See the Wireless World Authors:Hang Zou, Yu Tian, Bohao Wang, Lina Bariah, Samson Lasaulce, Chongwen Huang, Mérouane Debbah View a PDF of the paper titled RF-GPT: Teaching AI to See the Wireless World, by Hang Zou and 6 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) and multimodal models have become powerful general-purpose reasoning systems. However, radio-frequency (RF) signals, which underpin wireless systems, are still not natively supported by these models. Existing LLM-based approaches for telecom focus mainly on text and structured data, while conventional RF deep-learning models are built separately for specific signal-processing tasks, highlighting a clear gap between RF perception and high-level reasoning. To bridge this gap, we introduce RF-GPT, a radio-frequency language model (RFLM) that utilizes the visual encoders of multimodal LLMs to process and understand RF spectrograms. In this framework, complex in-phase/quadrature (IQ) waveforms are mapped to time-frequency spectrograms and then passed to pretrained visual encoders. The resulting representations are injected as RF tokens into a decoder-only LLM, which generates RF-grounded answers, explanations, and structured outputs. To train RF-GPT, we perform supervised instruction fine-tuning of a pretrained multimodal LLM using a fully sy...
