arXiv cs.AI· Fengchong Yao, Jianbing Li, Qing Liu, Qikun Liu, Kefeng Song, Haitao Li, Song Wang·· 4 小时前AI 评分9
PISA-CAPC：物理引导结构锚定与捕获感知原型校准用于跨环境射频指纹识别（论文已撤回）
Physics-Informed Structure Anchoring With Capture-Aware Prototype Calibration for Cross-Environment RF Fingerprinting
AI 导读
作者 Fengchong Yao 撤回了提出 PISA-CAPC 方法的射频指纹识别（RFFI）论文。该方法结合物理引导结构锚定与无标签捕获感知原型校准，在含 4 根接收天线、10 个发射器的 WiFi 数据集上，跨环境目标域 Macro-F1 达 0.9257。撤回原因仅注明"data needs to change"，无 PDF 可供下载。
正文 · 原文
This paper has been withdrawn by Fengchong Yao
No PDF available, click to view other formats
Abstract: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.
| Comments: | data needs to change |
| Subjects: | Signal Processing (eess.SP); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.09760 [eess.SP] |
| (or arXiv:2607.09760v3 [eess.SP] for this version) | |
| https://doi.org/10.48550/arXiv.2607.09760 arXiv-issued DOI via DataCite |
Submission history
From: Fengchong Yao [view email]
[v1]
Mon, 6 Jul 2026 13:23:50 UTC (1,697 KB)
[v2]
Tue, 14 Jul 2026 11:55:07 UTC (1,688 KB)
[v3]
Mon, 26 Oct 2026 07:11:50 UTC (1 KB) (withdrawn)
来源：arXiv cs.AI · arxiv.org