Radio Frequency Fingerprinting Research Articles

Few‐shot cross‐receiver radio frequency fingerprinting identification based on feature separation

AbstractRadio frequency fingerprint identification (RFFI) is a widely used technique for authenticating equipment. It identifies transmitters by extracting hardware defects found in the RF front end. Recent research has focused on the impact of transmitters and wireless channels on radio frequency fingerprint (RFF). Most work is based on the same receiver assumption, while the influence of the receiver on RFF remains unresolved. This paper focuses on the impact of receiver hardware characteristics on RFF and proposes a few‐shot cross‐receiver RFFI method based on feature separation. Data augmentation with noise addition and simulated channels addresses sparse sample issues and enhances the model's robustness to channel variations. Simultaneously, feature separation is realized by reducing the correlation between transmitter and receiver features through classification loss and similarity loss. We evaluate the proposed approaches using a large‐scale WiFi dataset. It is shown that when a trained transmitter classifier is deployed on new receivers with only 30 samples per trained transmitter, the average identification accuracy of the proposed method is 83.6%. This accuracy is 9.45% higher than the baseline method without considering transmitter hardware influence. After fine‐tuning, the average identification accuracy can reach 98.25%.

Dataset Augmentation and Fractional Frequency Offset Compensation Based Radio Frequency Fingerprint Identification in Drone Communications

Dataset Augmentation and Fractional Frequency Offset Compensation Based Radio Frequency Fingerprint Identification in Drone Communications

Progressive Unsupervised Domain Adaptation for Radio Frequency Signal Attribute Recognition across Communication Scenarios

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a progressive maximum similarity-based unsupervised domain adaptation (PMS-UDA) method for RF signal attribute recognition. First, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, a domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenarios. The experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as across both ground-to-ground and air-to-ground scenarios, compared to baseline methods.

Radio frequency fingerprint recognition method based on prior information

The open wireless communication environment is vulnerable to various malicious attacks. Wireless communication hardware devices have unique physical layer characteristics. As an inherent unique feature of wireless signals, radio frequency fingerprints provide a guarantee for the identification and verification of wireless signals. Most of the existing radio frequency fingerprint identification methods only extract fingerprints from one of the steady-state signals or transient signals. Neglecting the connection between the two wireless communication signals results in low identification accuracy of the radio frequency fingerprint identification method under the condition of a low signal-to-noise ratio. Aiming at the respective characteristics of these two signals, a radio frequency fingerprinting method combining transient and steady-state signals based on prior information of wireless signals is proposed. This method combines the characteristic stability of steady-state signals and the integrity characteristics of transient signals, which can effectively identify and classify wireless signals and achieve excellent recognition under low signal-to-noise ratio conditions. The effectiveness of the proposed method is verified by experimental comparison with the traditional radio frequency fingerprinting method on the LFM signal dataset.

Composite Ensemble Learning Framework for Passive Drone Radio Frequency Fingerprinting in Sixth-Generation Networks.

The rapid evolution of drone technology has introduced unprecedented challenges in security, particularly concerning the threat of unconventional drone and swarm attacks. In order to deal with threats, drones need to be classified by intercepting their Radio Frequency (RF) signals. With the arrival of Sixth Generation (6G) networks, it is required to develop sophisticated methods to properly categorize drone signals in order to achieve optimal resource sharing, high-security levels, and mobility management. However, deep ensemble learning has not been investigated properly in the case of 6G. It is anticipated that it will incorporate drone-based BTS and cellular networks that, in one way or another, may be subjected to jamming, intentional interferences, or other dangers from unauthorized UAVs. Thus, this study is conducted based on Radio Frequency Fingerprinting (RFF) of drones identified to detect unauthorized ones so that proper actions can be taken to protect the network's security and integrity. This paper proposes a novel method-a Composite Ensemble Learning (CEL)-based neural network-for drone signal classification. The proposed method integrates wavelet-based denoising and combines automatic and manual feature extraction techniques to foster feature diversity, robustness, and performance enhancement. Through extensive experiments conducted on open-source benchmark datasets of drones, our approach demonstrates superior classification accuracies compared to recent benchmark deep learning techniques across various Signal-to-Noise Ratios (SNRs). This novel approach holds promise for enhancing communication efficiency, security, and safety in 6G networks amidst the proliferation of drone-based applications.

Optimized Radio Frequency Footprint Identification Based on UAV Telemetry Radios.

With the widespread use of unmanned aerial vehicles (UAVs), the detection and identification of UAVs is a vital security issue for the safety of airspace and ground facilities in the no-fly zone. Telemetry radios are important wireless communication devices for UAVs, especially in UAVs beyond the visual line of sight (BVLOS) operating mode. This work focuses on the UAV identification approach using transient signals from UAV telemetry radios instead of the signals from UAV controllers that the former research work depended on. In our novel UAV Radio Frequency (RF) identification system framework based on telemetry radio signals, the EC-α algorithm is optimized to detect the starting point of the UAV transient signal and the detection accuracy at different signal-to-noise ratios (SNR) is evaluated. In the training stage, the Convolutional Neural Network (CNN) model is trained to extract features from raw I/Q data of the transient signals with different waveforms. Its architecture and hyperparameters are analyzed and optimized. In the identification stage, the extracted transient signals are clustered through the Self-Organizing Map (SOM) algorithm and the Clustering Signals Joint Identification (CSJI) algorithm is proposed to improve the accuracy of RF fingerprint identification. To evaluate the performance of our proposed approach, we design a testbed, including two UAVs as the flight platform, a Universal Software Radio Peripheral (USRP) as the receiver, and 20 telemetry radios with the same model as targets for identification. Indoor test results show that the optimized identification approach achieves an average accuracy of 92.3% at 30 dB. In comparison, the identification accuracy of SVM and KNN is 69.7% and 74.5%, respectively, at the same SNR condition. Extensive experiments are conducted outdoors to demonstrate the feasibility of this approach.

A channel resilient RFF extraction scheme for cyclic prefix contained systems

Radio frequency fingerprint (RFF) based identification technique has been proved efficient for ensuring the validity of devices connected to network. However, it is still a tough task to extract robust RFF in the scenarios with multi-path channel and moving terminals. To solve this problem, this paper proposes a channel-resilient RFF extraction scheme which can effectively reduce the influences from complex channel condition and retain robust device fingerprint. In the proposed system, blind synchronization and symbol-scale carrier frequency offset (CFO) estimation are designed for signal preprocessing for preparations of the following RFF extraction. A cyclic-prefix based de-channel algorithm (CPDCA) which can effectively weaken channel interference is proposed to meet the channel robustness of our system. Additionally, symbol-scale feature stacking algorithm (SFSA) is applied for RFF denoising, which can further enhance the performance of proposed system. Experiments using practical dataset collected from Long Term Evolution (LTE)-V2X communication system has been carried out under different signal-to-noise ratio (SNR). The results demonstrate that the proposed scheme has the ability to extract channel-robust RFF and to achieve reliable classification performance under complex channel conditions.

Efficient feature extraction of radio-frequency fingerprint using continuous wavelet transform

Efficient feature extraction of radio-frequency fingerprint using continuous wavelet transform

A Comprehensive Survey on Deep Learning-Based LoRa Radio Frequency Fingerprinting Identification.

LoRa enables long-range communication for Internet of Things (IoT) devices, especially those with limited resources and low power requirements. Consequently, LoRa has emerged as a popular choice for numerous IoT applications. However, the security of LoRa devices is one of the major concerns that requires attention. Existing device identification mechanisms use cryptography which has two major issues: (1) cryptography is hard on the device resources and (2) physical attacks might prevent them from being effective. Deep learning-based radio frequency fingerprinting identification (RFFI) is emerging as a key candidate for device identification using hardware-intrinsic features. In this paper, we present a comprehensive survey of the state of the art in the area of deep learning-based radio frequency fingerprinting identification for LoRa devices. We discuss various categories of radio frequency fingerprinting techniques along with hardware imperfections that can be exploited to identify an emitter. Furthermore, we describe different deep learning algorithms implemented for the task of LoRa device classification and summarize the main approaches and results. We discuss several representations of the LoRa signal used as input to deep learning models. Additionally, we provide a thorough review of all the LoRa RF signal datasets used in the literature and summarize details about the hardware used, the type of signals collected, the features provided, availability, and size. Finally, we conclude this paper by discussing the existing challenges in deep learning-based LoRa device identification and also envisage future research directions and opportunities.

Towards Receiver-Agnostic and Collaborative Radio Frequency Fingerprint Identification

Towards Receiver-Agnostic and Collaborative Radio Frequency Fingerprint Identification

An Authentication Mechanism Based on Zero Trust With Radio Frequency Fingerprint for Internet of Things Networks

An Authentication Mechanism Based on Zero Trust With Radio Frequency Fingerprint for Internet of Things Networks

Equalization-Assisted Domain Adaptation for Radio Frequency Fingerprint Identification

Equalization-Assisted Domain Adaptation for Radio Frequency Fingerprint Identification

Mitigating Receiver Impact on Radio Frequency Fingerprint Identification via Domain Adaptation

Mitigating Receiver Impact on Radio Frequency Fingerprint Identification via Domain Adaptation

Radio frequency fingerprint authentication based on feature fusion and contrastive learning

Radio frequency fingerprints (RFFs) are inherent attributes of wireless devices caused by differences in electronic components, which can serve as unique device identifiers and effective means of achieving physical layer authentication. However, traditional RFF approaches predominantly depend on manual feature extraction, which struggles to effectively distinguish devices due to its limited generalization capability as the number of devices grows. Additionally, current machine learning-based methods are designed for scenarios with a fixed set of trained devices while they fail to handle unknown devices. In this paper, we propose a novel contrastive learning-based RFF authentication framework to address the above challenges. The proposed framework integrates both manual feature extraction and deep learning-based techniques to enhance the generalization ability of RFFs, thereby enabling efficient differentiation among a vast number of devices. To further address the limitations of traditional machine learning approaches in dealing with unknown devices, we incorporate contrastive learning techniques into RFF authentication. By employing an input construction strategy, we generate positive and negative sample pairs of the RFFs to train an RFF compression network, allowing it to learn pivotal features within the fused RFFs. This process results in highly discriminative representations of the fused RFFs, ensuring effective authentication of open-set devices. The experimental results demonstrate that our method achieves an accuracy of 98% in open-set device classification even in the most complex experimental scenario, significantly outperforming other benchmark methods.

A RSBU-LSTM network for radio frequency fingerprint identification relying on multiple features

Radio frequency fingerprint identification (RFFI) can distinguish highly similar wireless communication devices to protect physical layer security and improve the security of wireless networks effectively, which has been widely used for spectrum management and physical layer secure communication. However, most RFFI methods show a degradation of performance under low signal-to-noise ratio (SNR) environments. In this paper, we propose a RSBU-LSTM network relying on multiple features to improve the identification accuracy with low SNR. Firstly, we use multiple features of in-phase (I), quadrature (Q), and phase as inputs. Then, we use multiple Residual Shrinkage Building Units (RSBUs) to extract the correlation features within the cycle of signals and preserve as many features as possible in low SNR environments. Finally, we use the long short-term memory (LSTM) to extract the relevant features of the signals of non-adjacent cycles. The experimental results show that the proposed network can effectively complete RFFI in low SNR environments and show better performance than other models used for comparison.

A novel sparse-based approach for joint radio frequency fingerprint and channel estimation

Radio Frequency Fingerprint (RFF) plays a pivotal role in specific emitter identification (SEI) and enhancing the physical layer security of wireless networks. However, past research on RFF extraction has encountered significant challenges, including algorithm instability in the presence of multi-path channels, disparities between actual RFF and proposed models, and the need to compensate for receiver RFF. In this study, we introduce an innovative algorithm designed to simultaneously estimate the transmitter's RFF and the non-line-of-sight (NLOS) channel state in the presence of receivers’ antenna effect. Moreover, we introduce the transmitter antenna feeding current as a modified RFF, accounting for transimtter components such as power amplifier (PA) nonlinearity, in-phase quadrature (IQ) modulator imbalance. Our approach commences with an exploration of wave propagation theory fundamentals, followed by a transformation of the inverse scattering equations into linear equations for joint channel and RFF estimation. Due to the non-convex nature of the derived equation, we employ the sparse lift method to render the problem convex. Leveraging the sparsity of channel coefficients, we devise a compressive sensing linear solution, denoted as the sparse-based approach for joint (SBJ) RFF and channel estimator. Additionally, we propose a calibration method to mitigate the effect of the receiver antennas on the estimated RFF of the transmitter. Through simulation, we demonstrate a 10 dB enhancement in RFF estimation accuracy compared to existing methods. Furthermore, our results indicate that the SBJ algorithm remains robust when faced with the combined effect of transmitter components, whereas previous models exhibit diminished precision as additional component effects are considered.

LoRa Radio Frequency Fingerprinting with Residual of Variational Mode Decomposition and Hybrid Machine-Learning/Deep-Learning Optimization

Radio Frequency Fingerprinting (RFF) refers to the technique for identifying and classifying wireless devices on the basis of their physical characteristics, which appear in the digital signal transmitted in space. Small differences in the radio frequency front-end of the wireless devices are generated across the same wireless device model during the implementation and manufacturing process. These differences create small variations in the transmitted signal, even if the wireless device is still compliant with the wireless standard. By using data analysis and machine-learning algorithms, it is possible to classify different electronic devices on the basis of these variations. This technique has been well proven in the literature, but research is continuing to improve the classification performance, robustness to noise, and computing efficiency. Recently, Deep Learning (DL) has been applied to RFF with considerable success. In particular, the combination of time-frequency representations and Convolutional Neural Networks (CNN) has been particularly effective, but this comes at a great computational cost because of the size of the time-frequency representation and the computing time of CNN. This problem is particularly challenging for wireless standards, where the data to be analyzed is extensive (e.g., long preambles) as in the case of the LoRa (Long Range) wireless standard. This paper proposes a novel approach where two pre-processing steps are adopted to (1) improve the classification performance and (2) to decrease the computing time. The steps are based on the application of Variational Mode Decomposition (VMD) where (in opposition to the known literature) the residual of the VMD application is used instead of the extracted modes. The concept is to remove the modes, which are common among the LoRa devices, and keep with the residuals the unique intrinsic features, which are related to the fingerprints. Then, the spectrogram is applied to the residual component. Even after this step, the computing complexity of applying CNN to the spectrogram is high. This paper proposes a novel step where only segments of the spectrogram are used as input to CNN. The segments are selected using a machine-learning approach applied to the features extracted from the spectrogram using the Local Binary Pattern (LBP). The approach is applied to a recent LoRa radio frequency fingerprinting public data set, where it is shown to significantly outperform the baseline approach based on the full use of the spectrogram of the original signal in terms of both classification performance and computing complexity.

A Novel RSS Fingerprint to Locate User Equipment UE in Remote Area

Location determination algorithms are widely used in cellular networks, especially in the long-term evolution (LTE) network, to enable the provision of location-based services (LBS). The increasing global demand for cellular networks has resulted in the creation of new user equipment (UE) positioning systems that align with the network's momentum. Regrettably, all of these technologies are hindered by their incapacity to ascertain the location of the UE in distant regions. This article introduces a novel method utilizing Radio Frequency (RF) fingerprinting to precisely locate UEs in remote areas. The approach entails employing a proposed partitioning model with a high level of precision, incorporating artificial intelligence and machine learning AI/ML in its fundamental state to reduce the search area. Furthermore, two algorithms are suggested: The first aims to enhance the efficiency of the battery with limited capacity by decreasing the frequency of measurements transmission. The second utilizes Jaccard similarity and incorporates the prefix filtering technique to determine matches. The algorithm is used to speed up the process of matching the fingerprint recorded in the fingerprint database with the fingerprint captured in real time. The results shows that it can reduces the transmission rate by 77.08% and achieves the lowest error rate of 35.34 m. Additionally, it exhibits a response time of 8 seconds.

Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprinting

Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprinting

Triplet Network and Unsupervised-Clustering-Based Zero-Shot Radio Frequency Fingerprint Identification With Extremely Small Sample Size

Triplet Network and Unsupervised-Clustering-Based Zero-Shot Radio Frequency Fingerprint Identification With Extremely Small Sample Size