Radio Frequency Fingerprint Identification Based on Metric Learning

Introduction

Radio-frequency fingerprint (RFF) is the intrinsic characteristics of wireless devices generated from hardware imperfection. Because hardware imperfection is unique for different wireless devices, RFF identification has become an emerging device authentication technique (Danev et al., 2012).

In general, RFF identification includes two steps: feature extraction and classification. Feature extraction determines the quality of RFF and directly affects classification accuracy. Many studies have explored the characteristics of different electronic components to extract effective RFF features—for example, in-phase and quadrature offset (Brik et al., 2008), phase offset (Nguyen et al., 2011), carrier frequency offset (Wheeler et al., 2017), differential constellation trace figure (Peng et al., 2019), and signal spectrum (Rehman et al., 2014). Recently, Wang et al. (2016) built a theoretical model for the entire wireless communication link to analyze the effectiveness of different RFF features. The results show that power spectrum density (PSD) can characterize the nonlinearity of the RF front end, contributing to the most significant RFF feature.

On the other hand, classification algorithm design is another key part of RFF identification in which lots of machine learning algorithms have been used. Danev et al. (2009) successfully classified 50 radio-frequency identification (RFID) transponders using principal component analysis (PCA) and -nearest neighbor (KNN). Baldini et al. (2017) compared the performance of KNN, support vector machine (SVM), and decision tree algorithm. Wang et al. (2017) used the Fisher linear discriminant analysis (LDA) based on the Mahalanobis distance metric to analyze the user capacity of wireless physical-layer identification. However, the performance of existing classification algorithms in RFF identification will severely degrade with the decrease of receive SNR. For example, six devices are classified with 98% accuracy under 30 dB and 90% accuracy under 10 dB (Patel et al., 2014). The results achieved only 51% identification accuracy for non-line-of-sight (NLOS) channel model (Wang et al., 2016), where the SNR is 15 dB. Peng et al. (2019) shared a method based on convolutional neural network (CNN) that can achieve 99.1% accuracy at high SNR but quickly dropped to 80% at 10 dB.

We propose a new RFF identification method based on metric learning (Weinberger et al., 2009) that can adapt to different SNRs. We used the large margin nearest neighbor (LMNN) to directly learn the optimal distance metric from training samples that have not been used in existing RFF identification works. Moreover, we propose a new training and test strategy based on mixed SNR that significantly improves the performance of conventional RFF identification methods with low complexity. We describe designing the real testbeds where eight devices are used for identification under different SNRs. The experiment results show that the LMNN algorithm achieves higher identification accuracy at low SNR than existing algorithms—for example, 96.83% accuracy at 10 dB.

The main contributions of this article are summarized as follows.