🤖 AI Summary
This work addresses the significant performance degradation of multi-antenna RF fingerprinting when deployed across diverse environments, primarily caused by discrepancies in receiver array topology, dynamic carrier frequency offsets (CFO), and capture-dependent variations. To mitigate these challenges, the authors propose the PISA-CAPC framework, which uniquely integrates array topology graphs with CFO dynamics to construct physics-informed structural anchors. Furthermore, they introduce an unsupervised Capture-Aware Prototype Calibration (U-CAPC) mechanism that operates without target-domain labels or updates to the backbone network, effectively decoupling source-domain representation learning from target-domain decision calibration. Evaluated on a real-world multi-antenna Wi-Fi dataset comprising ten transmitters, the method achieves an average Macro-F1 score of 0.9257 under balanced conducted settings, demonstrating the complementary benefits and efficacy of its constituent components.
📝 Abstract
Radio frequency fingerprint identification (RFFI) uses transmitter-specific hardware imperfections as a physicallayer identity cue for Internet of Things (IoT) devices, but deep RFFI models often degrade when the acquisition environment changes. In multi-antenna reception, this degradation is not merely a generic distribution shift. It is also shaped by receiver-array topology, frequency-offset dynamics, and capturedependent target structure, which can distort embeddings and move source-trained decision boundaries. This article proposes physics-informed structure anchoring with capture-aware prototype calibration (PISA-CAPC), a framework that separates source representation anchoring from fixed-backbone target calibration. The representation stage organizes antenna tokens with a topology graph and modulates the graph using CFO-derived acquisition-dynamics descriptors. Bounded contextual residual suppression is then applied around the identity representation. At deployment, unlabeled capture-aware prototype calibration (U-CAPC) calibrates target decision scores through capturelocal prototype evidence under a fixed representation, mitigating boundary shift without requiring target-domain backbone updates or target labels. On a measured ten-transmitter multiantenna WiFi benchmark, PISA-CAPC achieves 0.9257 targetdomain mean Macro-F1 under a balanced transductive setting. Ablations confirm that topology-guided structure anchoring, contextual residual suppression, and capture-aware calibration contribute complementary gains. These results establish PISACAPC as a fixed-backbone route to cross-environment RFFI, coupling physically motivated representation learning with labelfree, capture-aware decision calibration.