EquiRFF: Radio Frequency Fingerprint Identification via Physical Equivariance Guided Representation Learning

Radio frequency fingerprint identification (RFFI) seeks to preserve device-specific hardware cues under changing acquisition conditions, yet these cues are often entangled with domain factors in radio frequency (RF) signals. Overly strong invariance may suppress useful structural cues, while purely supervised models can exploit source-domain shortcuts. We propose EquiRFF, a two-stage framework that learns stable device identity from physical equivariance without explicit nuisance-factor separation. Stage I learns how structured physical transformations act in latent space and enforces consistency between sequential and composed transformations. Stage II extracts device-discriminative information that remains stable across these equivariant representations while retaining transformation information in the encoder. Physical views and condition parameters are used only during training. At inference, EquiRFF requires only a single I/Q observation, without physical-condition inputs or target-domain adaptation. Experiments on four public RF datasets show strong cross-domain performance against recent strong baselines.