TrustAgent-RFF: Agentic Diagnostic Planning for Reliable RF Fingerprinting under Domain Shift
Read the original on arXiv Machine Learning →TrustAgent‑RFF is a framework that enhances the reliability of radio frequency fingerprinting (RFF) models when faced with domain shifts such as changes in receiver, distance, or environment. It operates on a frozen I/Q classifier and gathers diagnostic evidence—counterfactual stability, prototype consistency, acquisition domain similarity, and signal quality—to estimate prediction reliability. A large language model planner selects diagnostic actions based on evidence, trust state, costs, and budget, while a calibrated decision layer classifies predictions as Accept, Reject, or Defer. Experiments on five RFF datasets show a 38.7% drop in source‑to‑target accuracy and highlight that many incorrect target‑domain predictions remain highly confident.
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