Mitigating Receiver Impact on Radio Frequency Fingerprint Identification via Domain Adaptation

• URL: http://arxiv.org/abs/2404.08566v1
• Date: Fri, 12 Apr 2024 16:08:32 GMT
• Title: Mitigating Receiver Impact on Radio Frequency Fingerprint Identification via Domain Adaptation
• Authors: Liu Yang, Qiang Li, Xiaoyang Ren, Yi Fang, Shafei Wang,
• Abstract summary: We develop a theoretical generalization error bound for the adaptation model. Motivated by the bound, we propose a novel method to solve the cross-receiver RFFI problem, which includes domain alignment and adaptive pseudo-labeling. Experimental results indicate that the proposed method can effectively mitigate the receiver impact and improve the cross-receiver RFFI performance.
• Score: 15.347306554562048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Radio Frequency Fingerprint Identification (RFFI), which exploits non-ideal hardware-induced unique distortion resident in the transmit signals to identify an emitter, is emerging as a means to enhance the security of communication systems. Recently, machine learning has achieved great success in developing state-of-the-art RFFI models. However, few works consider cross-receiver RFFI problems, where the RFFI model is trained and deployed on different receivers. Due to altered receiver characteristics, direct deployment of RFFI model on a new receiver leads to significant performance degradation. To address this issue, we formulate the cross-receiver RFFI as a model adaptation problem, which adapts the trained model to unlabeled signals from a new receiver. We first develop a theoretical generalization error bound for the adaptation model. Motivated by the bound, we propose a novel method to solve the cross-receiver RFFI problem, which includes domain alignment and adaptive pseudo-labeling. The former aims at finding a feature space where both domains exhibit similar distributions, effectively reducing the domain discrepancy. Meanwhile, the latter employs a dynamic pseudo-labeling scheme to implicitly transfer the label information from the labeled receiver to the new receiver. Experimental results indicate that the proposed method can effectively mitigate the receiver impact and improve the cross-receiver RFFI performance.

Related papers

• Radar Signal Recognition through Self-Supervised Learning and Domain Adaptation [48.265859815346985]
  We introduce a self-supervised learning (SSL) method to enhance radar signal recognition in environments with limited RF samples and labels. Specifically, we investigate pre-training masked autoencoders (MAE) on baseband in-phase and quadrature (I/Q) signals from various RF domains. Results show that our lightweight self-supervised ResNet model with domain adaptation achieves up to a 17.5% improvement in 1-shot classification accuracy.
  arXiv  Detail & Related papers  (2025-01-07T01:35:56Z)
• Rydberg Atomic Quantum Receivers for Classical Wireless Communications and Sensing: Their Models and Performance [78.76421728334013]
  Rydberg atomic quantum receivers (RAQRs) are an eminent solution for detecting the electric field of radio frequency (RF) signals. We introduce the superheterodyne version of RAQRs to the wireless community by presenting an end-to-end reception scheme. We then develop a corresponding equivalent baseband signal model relying on a realistic reception flow.
  arXiv  Detail & Related papers  (2024-12-07T06:25:54Z)
• RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge [66.33067693672696]
  This paper addresses the critical problem of interference rejection in radio-frequency (RF) signals using a novel, data-driven approach. First, we present an insightful signal model that serves as a foundation for developing and analyzing interference rejection algorithms. Second, we introduce the RF Challenge, a publicly available dataset featuring diverse RF signals along with code templates. Third, we propose novel AI-based rejection algorithms, specifically architectures like UNet and WaveNet, and evaluate their performance across eight different signal mixture types.
  arXiv  Detail & Related papers  (2024-09-13T13:53:41Z)
• Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer [60.31021888394358]
  Unsupervised Domain Adaptation (UDA) can effectively address domain gap issues in real-world image Super-Resolution (SR) We propose a SOurce-free Domain Adaptation framework for image SR (SODA-SR) to address this issue, i.e., adapt a source-trained model to a target domain with only unlabeled target data.
  arXiv  Detail & Related papers  (2023-03-31T03:14:44Z)
• One-shot Generative Distribution Matching for Augmented RF-based UAV Identification [0.0]
  This work addresses the challenge of identifying Unmanned Aerial Vehicles (UAV) using radiofrequency (RF) fingerprinting in limited RF environments. The complexity and variability of RF signals, influenced by environmental interference and hardware imperfections, often render traditional RF-based identification methods ineffective. One-shot generative methods for augmenting transformed RF signals offer a significant improvement in UAV identification.
  arXiv  Detail & Related papers  (2023-01-20T02:35:43Z)
• 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)
• Unsupervised Domain Adaptation for RF-based Gesture Recognition [14.523667781155666]
  We propose an unsupervised domain adaptation framework for RF-based gesture recognition. We first propose pseudo-labeling and consistency regularization to utilize unlabeled data for model training. We then design two corresponding data augmentation methods based on the characteristic of the RF signals to strengthen the performance of the consistency regularization.
  arXiv  Detail & Related papers  (2021-11-20T14:25:35Z)
• 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)

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.