Where You Tap Matters: A Probe-and-Model Benchmark for Open-Set RF Fingerprinting

• URL: http://arxiv.org/abs/2607.21564v1
• Date: Thu, 23 Jul 2026 17:48:38 GMT
• Title: Where You Tap Matters: A Probe-and-Model Benchmark for Open-Set RF Fingerprinting
• Abstract summary: Radio Frequency Fingerprint Identification (RFFI) enables transmitter identification at the physical layer by learning device-specific impairments from received signals.<n>We present a systematic real-world evaluation of open-set, reconstruction-error RFFI using data collected at five probe points along a standard BPSK receiver chain.
• Score: 2.0299248281970956
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Radio Frequency Fingerprint Identification (RFFI) enables transmitter identification at the physical layer by learning device-specific impairments from received signals, yet the literature is inconsistent about where in the receiver chain those samples should be collected. Since distinct transformations are applied to the signal by the different receiver operations, i.e., carrier recovery, gain normalization, pulse shaping, and timing recovery, they can either tighten within-transmitter variability or suppress the features RFFI requires for classification. We present a systematic real-world evaluation of open-set, reconstruction-error RFFI using data collected at five probe points along a standard BPSK receiver chain. Our results show that RFFI is strongly probe-dependent: timing recovery and, to a lesser extent, carrier recovery enable low false-acceptance operation with limited in-distribution-out-of-distribution overlap, whereas other stages often require a false-acceptance ratio above 0.1 to achieve a true-acceptance ratio of 0.9. To test the validity of our findings across model selection, we benchmark several LLM-designed autoencoders using a controlled pipeline that holds preprocessing and MSE scoring fixed. These architectures confirm that RFFI is probe-dependent. Moreover, they do not outperform the baseline at the chosen operating point and typically increase training time. Overall, probe selection dominates reconstruction-based open-set RFFI performance, more than the autoencoder complexity.

Related papers

• S$^3$AM: A Single-Stream SAM with Reliability-Calibrated Frequency Adapter for Multi-modal Salient Object Detection [74.15663758681849]
  Single-stream framework integrates reliability-calibrated frequency adaptation into the adopted SAM backbone for MSOD.<n>Experiments on RGB-D, RGB-T, and RGB-NIR salient object detection benchmarks validate that the proposed framework achieves competitive performance.
  arXiv  Detail & Related papers  (2026-08-18T08:05:00Z)
• HoRFFI: High-Openness RF Fingerprint Identification with a Similarity-Enhanced Variational Information Bottleneck [17.050930905392374]
  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.
  arXiv  Detail & Related papers  (2026-08-05T14:07:52Z)
• Cross-Receiver Open-Set Radio Frequency Fingerprinting via Structure-First Adaptation [9.457137244448043]
  This paper presents a Cross-Receiver Open-set Domain Adaptation framework via Structure-first Training (CRODA-ST) for RFFI.<n> Discriminative Structure Anchoring restores target-receiver known-class references from limited labeled target enrollment samples.<n>On the WiSig ManyTx dataset, CRODA-ST achieves known-class accuracy, 0.9692 area under the receiver operating characteristic curve (AUROC), 0.9580 open-set classification rate (OSCR), and a false positive rate of 0.0469 at a 90% true positive rate (FPR90)
  arXiv  Detail & Related papers  (2026-06-30T03:56:42Z)
• Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols [123.73663884421272]
  Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.<n>We establish FEWTRANS, a comprehensive benchmark containing 10 diverse datasets.<n>By releasing FEWTRANS, we aim to provide a rigorous "ruler" to streamline reproducible advances in few-shot transfer learning research.
  arXiv  Detail & Related papers  (2026-02-28T05:41:57Z)
• Exploiting Radio Frequency Fingerprints for Device Identification: Tackling Cross-receiver Challenges in the Source-data-free Scenario [17.211137756661955]
  We present a source-data-free cross-receiver RFFI problem, where a model pretrained on labeled signals from a source receiver must adapt to unlabeled signals from a target receiver.<n>We propose Momentum Soft pseudo-label Source Hypothesis Transfer (MS-SHOT), a new method for SCRFFI that incorporates momentum-center-guided soft pseudo-labeling and enforces global structural constraints.<n>MS-SHOT consistently outperforms existing approaches in both accuracy and robustness, offering a practical and scalable solution for source-data-free cross-receiver adaptation in RFFI.
  arXiv  Detail & Related papers  (2025-12-18T15:20:33Z)
• Doppler Invariant CNN for Signal Classification [0.0]
  We propose a convolutional neural network (CNN) architecture with complex-valued layers that exploits convolutional shift equivariance in the frequency domain.<n>We show that our model maintains consistent classification accuracy with and without random Doppler shifts despite being trained on no Doppler-shifted examples.
  arXiv  Detail & Related papers  (2025-11-18T16:31:13Z)
• Source-Free Object Detection with Detection Transformer [59.33653163035064]
  Source-Free Object Detection (SFOD) enables knowledge transfer from a source domain to an unsupervised target domain for object detection without access to source data.<n>Most existing SFOD approaches are either confined to conventional object detection (OD) models like Faster R-CNN or designed as general solutions without tailored adaptations for novel OD architectures, especially Detection Transformer (DETR)<n>In this paper, we introduce Feature Reweighting ANd Contrastive Learning NetworK (FRANCK), a novel SFOD framework specifically designed to perform query-centric feature enhancement for DETRs.
  arXiv  Detail & Related papers  (2025-10-13T07:35:04Z)
• Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training [29.396203071880905]
  Radio frequency fingerprint identification (RFFI) is a critical technique for wireless network security.<n>Deep neural networks have shown remarkable capability in extracting discriminative features, but real-world deployment is hindered by receiver-induced variability.<n>We propose an RFFI framework robust to cross-receiver variability, integrating adversarial training and style transfer to explicitly disentangle transmitter and receiver features.<n>Our approach consistently outperforms state-of-the-art baselines, achieving up to a 10% improvement in average accuracy across diverse receiver settings.
  arXiv  Detail & Related papers  (2025-10-10T14:01:05Z)
• Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics [77.13542705329328]
  We propose a framework of disentangled representation learning(DRL) that first learns to factor the input signals into a device-relevant component and a device-irrelevant component via adversarial learning. The implicit data augmentation in the proposed framework imposes a regularization on the RFF extractor to avoid the possible overfitting of device-irrelevant channel statistics. Experiments validate that the proposed approach, referred to as DR-RFF, outperforms conventional methods in terms of generalizability to unknown complicated propagation environments.
  arXiv  Detail & Related papers  (2022-08-04T15:46:48Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authentication [74.63333951647581]
  Radio-frequency fingerprints(RFFs) are promising solutions for realizing low-cost physical layer authentication. Machine learning-based methods have been proposed for RFF extraction and discrimination. We propose a new end-to-end deep learning framework for extracting RFFs from raw received signals.
  arXiv  Detail & Related papers  (2021-08-10T03:59:37Z)
• Ensemble Wrapper Subsampling for Deep Modulation Classification [70.91089216571035]
  Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms. We propose a subsampling technique to facilitate the use of deep learning for automatic modulation classification in wireless communication systems.
  arXiv  Detail & Related papers  (2020-05-10T06:11:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.