TrustAgent-RFF: Agentic Diagnostic Planning for Reliable RF Fingerprinting under Domain Shift
We propose TrustAgent-RFF, a prediction level reliability framework for RFF that operates with a frozen in-phase and quadrature (I/Q) classifier without target domain labels.
ProofPaper ↗
Key points
- Radio frequency fingerprinting (RFF) models can achieve accuracy under source domain conditions but become unreliable when the receiver, distance, collection day, or environment changes.
- For each device prediction, the framework acquires radio frequency evidence: counterfactual stability, prototype consistency, acquisition domain similarity, and signal quality.
- A source calibrated trust estimator combines these diagnostics with classifier confidence, prediction margin, and evidence availability indicators.
- Experiments across five RFF datasets and classifier backbones reveal an average source to target accuracy drop of 38.7% and show that many incorrect target domain predictions remain highly confident.
Sources (1)
- [1]TrustAgent-RFF: Agentic Diagnostic Planning for Reliable RF Fingerprinting under Domain ShiftarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 12:56 AM
We propose TrustAgent-RFF, a prediction level reliability framework for RFF that operates with a frozen in-phase and quadrature (I/Q) classifier without target domain labels.
Radio frequency fingerprinting (RFF) models can achieve accuracy under source domain conditions but become unreliable when the receiver, distance, collection day, or environment changes.
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