# Physics-Informed Structure Anchoring With Capture-Aware Prototype Calibration for Cross-Environment RF Fingerprinting

arXiv: https://arxiv.org/abs/2607.09760 · HTML: https://mlsanity.com/paper/2607.09760/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 6 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprinting](https://mlsanity.com/paper/2303.10691/) (2023)
- [Domain Generalization for Cross-Receiver Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2411.03636/) (2024)
- [Semi-Supervised RF Fingerprinting with Consistency-Based Regularization](https://mlsanity.com/paper/2304.14795/) (2023)
- [Mitigating Receiver Impact on Radio Frequency Fingerprint Identification via Domain Adaptation](https://mlsanity.com/paper/2404.08566/) (2024)
- [Towards Robust RF Fingerprint Identification Using Spectral Regrowth and Carrier Frequency Offset](https://mlsanity.com/paper/2412.07269/) (2024)
- [Towards Channel-Robust and Receiver-Independent Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2512.12070/) (2025)
- [Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices](https://mlsanity.com/paper/2303.13538/) (2023)

## Abstract (arXiv)

Radio frequency fingerprint identification (RFFI) exploits transmitter-specific hardware imperfections as physicallayer identity cues for Internet of Things (IoT) devices, but deep models often degrade across acquisition environments. In multi-antenna reception, antenna topology and frequencyoffset dynamics structure receiver observations, while capturedependent variation distorts target embeddings and misaligns source-trained decision boundaries. This article proposes physicsinformed structure anchoring with capture-aware prototype calibration (PISA-CAPC) to address both representation and decision mismatches. The two stages separate source representation construction from target decision correction. During source training, PISA organizes antenna tokens through a topology-guided graph, conditions propagation on CFO-derived acquisition dynamics, and applies bounded contextual residual suppression to preserve identity evidence. At deployment, unlabeled capture-aware prototype calibration (U-CAPC) estimates capture-local prototypes and recalibrates target decision scores while keeping the representation and source classifier fixed. Thus, calibration uses neither target labels nor target-domain backbone updates. On a measured WiFi benchmark with four receive antennas and ten transmitters, PISA-CAPC achieves a mean target-domain Macro-F1 of 0.9257 under a balanced transductive setting. Component ablations support complementary roles for topology-guided anchoring, CFO-conditioned modulation, reliability-aware token aggregation, contextual suppression, and capture-aware calibration. These results indicate that physically motivated representation learning can be combined with labelfree decision calibration to improve cross-environment RFFI under the evaluated protocol without changing the deployed backbone.