Radio frequency fingerprint (RFF) is a promising solution for realizing secure and efficient device identification. However, the accuracy of currently existing solutions suffer from multipath effects in practical scenarios. In this paper, we provide a robust RFF identification method that leverages channel state information (CSI) feedback to counteract the effect of the channel on the extracted RFF features. A straightforward zero-forcing (ZF) equalization fails to fully decouple RF impairments from the channel, making conventional approaches ineffective. To overcome this challenge, we utilize the potential of multi-antenna and introduce a new device-specific feature called Relative-RFF (R-RFF), which represents the relation between different RF chains in a multi-antenna transmitter. We propose an enhanced ZF post-equalization algorithm to eliminate the multipath channels and preserve the users’ R-RFF to the greatest extent. We evaluate the robustness of R-RFF under various channel conditions and noise levels and the performance of R-RFF in terms of identification accuracy under different channel scenarios. The results show that the proposed R-RFF method can achieve an identification accuracy of 91.2% for 70 devices in tapped delay line channel with a signal-to-noise ratio (SNR) of 30 dB.

Radio frequency fingerprint (RFF) identification is a promising solution for Internet of Things (IoT) device authentication. However, this technique encounters practical challenges such as noise interference, channel coupling, and open-set recognition (OSR). This paper proposes a unified RFF-OSR framework to jointly address these problems in complex environments. Firstly, the framework mitigates the noise interference by employing a low-pass filter-integrated autoencoder, where the low-pass filter is used to obtain a “quasi-clean” signal as the autoencoder reference, thereby reducing the demand for ideal signals. Then, the channel influence on RFF is modeled as three types: frequency offset, phase noise, and amplitude distortion. Based on this model, parameterized channel augmentation is performed to improve the generalization ability of RFF identification in unknown channel scenarios. In terms of OSR, instead of a coarse-grained uniform probability threshold for rogue device recognition, we conduct independent similarity judgments for all legitimate classes, each with an individual threshold. It effectively reduces information loss in the feature probability transformation and increases OSR performance. Under additive white Gaussian noise (AWGN) and multipath channel conditions, our method achieves OSR accuracies of 99.37% and 97.05% in ZigBee device identification, respectively, which demonstrates the effectiveness of our approach.

Radio frequency fingerprinting (RFF) is a promising solution for realizing secure and efficient device authentication. The multipath channel overshadows and disrupts the RFF extraction, which causes difficulties in training new models in the presence of fading. Existing approaches attempt to deal with this challenge by traversing channels through simulated channel models. However, this solution requires a large amount of data for training and it is difficult to guarantee that the training covers all possible channels. To mitigate the multipath channel effect on RFF with less training data, we propose a new method in a multi-antenna system, named Relative-RFF (R-RFF), which utilizes channel state information (CSI) feedback to counteract the multipath channel. The RFF imperfection relation between the different antenna chains of the device is proved to be retained after the counteraction of the multipath channel. Numerical results demonstrate that the proposed R-RFF can achieve an identification accuracy of 95.9% for 30 UEs in Tapped Delay Line channel with a signal-to-noise ratio of 20 dB.

Radio frequency fingerprint (RFF) identification is an emerging device authentication technique that exploits the hardware imperfections resulting from the manufacturing process. Due to the varying impact of the wireless channel during RFF training and test stages, it is challenging to design channel-independent RFF techniques. This article designs a channel robust RFF identification scheme by leveraging the different spectrum of adjacent signal symbols, named the Difference of the Logarithm of the Spectrum (DoLoS), which does not rely on a single RFF feature or requires additional manipulation of the devices under test. Specifically, DoLoS exploits the fact that two different symbols in a packet exhibit different RFF features but have a similar channel response during the channel coherence time. We implemented the DoLoS with the IEEE 802.11 orthogonal frequency division multiplexing (OFDM) system as a case study. We carried out extensive experiments using seven Wi-Fi devices of the same model in different wireless channel environments, including 12 data collection positions in two completely different environments. Compared with conventional RFF identification schemes that do not eliminate channel effects, our scheme is robust to channel variations and the highest identification accuracy is 99.02% in the single-environment evaluation and 97.05% in the cross-environment evaluation.

Radio-frequency fingerprinting (RFF) exploiting hardware characteristics has been employed for device recognition to enhance the overall security. However, the performance unreliability in long-term experiments, channel fading interference, and unauthorized devices verification are three open problems that restrict the development of RFF recognition. To address these issues, a robust RFF extraction scheme based on three corresponding algorithms is studied. For the first problem, a long-term stacking of repetitive symbols (LSRSs) algorithm is proposed to reduce the acquired signal variance, which contributes to the identification accuracy and long-term stability. For the second issue, we propose an artificial noise adding (ANA) algorithm to enhance the recognition robustness through regularization and channel adaptation. For the third issue, a verification algorithm based on the generative Gaussian probabilistic linear discriminant analysis (GPLDA) model is developed to handle unauthorized devices. Our robust RFF extraction scheme is verified in the experiments with 54 CC2530 ZigBee devices. It enables reliable node identification with the accuracy of 99.50% in the short rang line-of-sight (SLOS) scenarios for signals collected over 18 months, and 95.52% in the extensive multipath fading experiments. The equal error rate (EER) of the verification experiments with six authorized devices versus six unseen unauthorized devices is as low as 0.63%.

Radar is an indispensable part of the Internet of Things (IoT). Specific emitter identification is essential to identify the legitimate radars and, more importantly, to reject the malicious radars. Conventional methods rely on pulse parameters that are not capable to identify the specific emitter as two radars may have the same configuration or a malicious radar can perform spoofing attacks. Radio-frequency fingerprint (RFF) is the unique and intrinsic hardware characteristic of devices resulted from hardware imperfection, which can be used as the device identity. This article proposes a robust and reliable radar identification scheme based on the RFF, taking linear frequency modulation (LFM) radar as a case study. This scheme first classifies the operation mode of the pulses, then eliminates the noise effect, and finally identifies the radar emitters based on the transient and modulation-based RFF features. The experimental results verify the effectiveness of our radar identification scheme among three real LFM radars (same model) operating at four modes, each mode with 2000 pulses from each radar. The identification rates of the four modes are all higher than 90% when the signal-to-noise ratio (SNR) is about 5 dB. In addition, mode 3 achieves almost 100% identification accuracy even when the SNR is as low as -10 dB.

Radar transmitter identification technology based on pulse descriptor word (PDW) is broadly used in military and civilian applications. However, as the complexity of the electromagnetic environment has increased, radar identification has been challenging. Radio frequency fingerprint (RFF) is an intrinsic hardware characteristic and has been widely employed for device identification. In this paper, we propose a robust RFF identification scheme for linear frequency modulation (LFM) pulse radars. The scheme includes a proposed piecewise curve fitting based denoising (PCFD) algorithm and a hybrid RFF identification algorithm. The PCFD algorithm can reduce the noise of LFM pulses without undermining RFF features. The hybrid RFF identification algorithm extracts both transient-based and modulation-based RFF features. Experimental results demonstrate that the proposed radar identification scheme can achieve a 100% identification accuracy when the SNR is about 0 dB.

Radio Frequency Fingerprinting (RFF) is one of the promising passive authentication approaches for improving the security of the Internet of Things (IoT). However, with the proliferation of low-power IoT devices, it becomes imperative to improve the identification accuracy at low SNR scenarios. To address this problem, this paper proposes a general Denoising AutoEncoder (DAE)-based model for deep learning RFF techniques. Besides, a partially stacking method is designed to appropriately combine the semi-steady and steady-state RFFs of ZigBee devices. The proposed Partially Stacking-based Convolutional DAE (PSC-DAE) aims at reconstructing a high-SNR signal as well as device identification. Experimental results demonstrate that compared to Convolutional Neural Network (CNN), PSCDAE can improve the identification accuracy by 14% to 23.5% at low SNRs (from -10 dB to 5 dB) under Additive White Gaussian Noise (AWGN) corrupted channels. Even at SNR = 10 dB, the identification accuracy is as high as 97.5%.