HoRFFI: High-Openness RF Fingerprint Identification with a Similarity-Enhanced Variational Information Bottleneck

• URL: http://arxiv.org/abs/2608.04881v1
• Date: Wed, 05 Aug 2026 14:07:52 GMT
• Title: HoRFFI: High-Openness RF Fingerprint Identification with a Similarity-Enhanced Variational Information Bottleneck
• Abstract summary: HoRFFI is a high-openness RFFI framework that supports scalable device identification and unknown-device rejection.<n> Experiments on public LoRa and Wi-Fi datasets show that HoRFFI achieves absolute improvements of (0.112) and (0.288) in novel-class accuracy.
• Score: 17.050930905392374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Radio frequency fingerprint identification (RFFI) is a promising technique for wireless device authentication. However, practical RFFI systems must enroll newly authorized devices while rejecting previously unseen ones, even when the feature extractor is trained on only a few labeled base-device classes, giving rise to a high-openness RFFI problem. Existing open-set recognition methods typically rely on feature spaces learned from a large and diverse set of known-device classes, limiting their applicability in practical scenarios. To address this challenge, we propose HoRFFI, a high-openness RFFI framework that supports scalable device identification and unknown-device rejection using only a small number of labeled training devices. HoRFFI employs a similarity-enhanced variational information bottleneck (SVIB)-based supervision mechanism, which reduces the encoder's dependence on training-class diversity and learns a more transferable embedding space. This supervision mechanism uses feature-space augmentation and clustering to derive inter-sample similarity information, which provides supplementary supervision for regularizing the embedding space. Experiments on public LoRa and Wi-Fi datasets show that HoRFFI achieves absolute improvements of \(0.112\) and \(0.288\) in novel-class accuracy, respectively, and corresponding absolute AUC improvements of \(0.029\) and \(0.060\) over the best-performing baselines.

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