Same Identity, Relaxed Evidence: MarginCell for RF Domain Generalization

Radio-frequency fingerprint identification (RFFI) provides lightweight physical-layer authentication solution in zero-trust wireless networks. In practical deployments, however, its recognition performance degrades sharply under unseen environments. Existing robust RFFI schemes follow a suppress-and-stabilize paradigm that seek to eliminate environment-induced variation, leaving residual-shift tolerance unmodeled. To tackle this, we reformulate source-only RF domain generalization via decision-evidence geometry. We prove that true-vs-rival margins form decision-complete evidence coordinates, under which each identity occupies a relaxed region of decision-equivalent states rather than a single point. This geometry yields a maximal reserve-preserving evidence cell and an exact bottleneck robustness radius, while revealing cross-entropy’s intrinsic contraction toward point evidence. Building upon this insight, we propose MarginCell, a source-only regularizer that combines a bounded dispersion reward for releasing decision-safe variation with a distance-to-cell barrier against reserve-eroding deviations. Experiments show consistent improvements in cross-environment recognition and complementary gains with diverse methods.