Radio Frequency Fingerprint (RFF) identification plays a critical role in physical-layer security by enabling the identification of wireless devices. Recent advances have leveraged deep learning (DL) to enhance performance and robustness. However, existing DL-based RFF identification methods rely heavily on large-scale labeled signal datasets, making data annotation costly and challenging, particularly in complex electromagnetic environments. To address this limitation, we propose a self-supervised RFF identification method based on Time-Frequency Contrastive Learning (TFCL), designed to operate on unlabeled signal samples. The framework consists of two key modules: (1) a time-frequency contrastive self-supervised learning module, which constructs robust RFF feature embeddings from unlabeled signals, and (2) a CutMix-based regularized finetuning module, which enhances robustness through regularized training. Moreover, we introduce parameter freezing integrated with CutMix to adapt to diverse downstream scenarios. Extensive experiments demonstrate that the proposed TFCL-based method achieves superior feature embedding quality and identification accuracy compared to four competitive baselines, highlighting its effectiveness in real-world applications.

With the increasing demand for secure communication in 5G and beyond, authentication of wireless devices has become a crucial task for communication security. Radio frequency fingerprint identification (RFFI) leverages the hardware-specific features in radio frequency (RF) signals, known as radio frequency fingerprints (RFF), to achieve highprecision device identification. However, the dependence of RFFI on the physical characteristics of devices makes it vulnerable to physical spoofing attacks. This paper proposes an innovative physical spoofing attack framework that combines spoofed transmitter and legitimate transmitter models. It performs RFF modeling, RFF concealment (RFFC), and RFF spoofing (RFFS) sequentially to achieve precise spoofing of the original baseband signal. We validate the effectiveness of the proposed physical spoofing mechanism through simulations of seven types of transmitters using MATLAB Simulink. The performance is further evaluated on an RFFI model based on complexvalued convolutional neural networks (CVCNN). Experimental results show that neural networks (NN) significantly outperform the generalized memory polynomial (GMP) model in nonlinear data fitting and temporal relationship modeling. Consequently, NN-based physical spoofing methods exhibit superior attack effectiveness. Specifically, under the signal-to-noise ratio (SNR) of 15 dB, the NN-based physical spoofing method achieves a target attack success rate (TSR) as high as 98%, which is superior to adversarial attack methods. NN-based methods also enhanced performance in terms of stealthiness metrics.

In this study, we present an innovative architecture for blind modulation-type identification in single-antenna channels, specifically engineered for scenarios lacking transmitter cooperation and featuring time-varying spectrum occupancy. The proposed method integrates a lightweight Transformer-based deep learning architecture with a higher-order statistics-driven noise reduction module, designed to enhance feature discrimination in environments with poor signal clarity. Comprehensive testing demonstrates that the proposed approach significantly improves classification accuracy from 52% to 75% at 0 dB. The model achieves this with a highly compact footprint of only 0.228 M parameters and minimal computational overhead (0.0095 GFLOPs), highlighting its excellent balance of robustness and efficiency. The model consistently performs well across a broad range of signal-to-noise ratio, validating its generalization capability in challenging environments. By addressing the critical challenges of automatic modulation classification in non-cooperative and spectrum-constrained contexts, this work offers a scalable and efficient solution that supports intelligent spectrum awareness and facilitates robust cognitive communication in next-generation wireless networks.

Radio frequency fingerprint identification (RFFI) is essential for ensuring device security in ubiquitous wireless communications and the Internet of Things (IoT). Although deep learning (DL) and self-supervised learning (SSL) techniques have significantly enhanced RFFI system performance, they often depend on data sourced from open internet datasets or collected in uncontrolled environments. This dependency heightens the risk of malicious data tampering, particularly in adversarial environment, thereby exacerbating security vulnerabilities. To address this issue, this paper investigates the vulnerability of SSL-based RFFI systems to backdoor attacks and introduces a novel method utilizing a spectrum-enhanced insensitive perturbation (SEIP) trigger. We develop an SSL-based RFFI backdoor attack framework, providing new insights into SSL security within the signal processing domain. The SEIP trigger introduces subtle perturbations in the frequency domain, enabling highly effective and covert backdoor attacks. Experimental results demonstrate that the SEIP trigger outperforms existing triggers regarding attack effectiveness across various channel conditions while maintaining strong stealthiness against backdoor defense mechanisms. These findings confirm that the SEIP trigger achieves an optimal balance between attack effectiveness and stealthiness. Moreover, this paper offers a new perspective for evaluating the security of RFFI systems and the resilience of backdoor defense strategies.

Radio frequency fingerprinting (RFF) serves as a distinctive hardware trait in transmitters, forming the cornerstone of transmitter identification. While recent advancements led to significant improvements in identification accuracy, these developments also inadvertently simplify the process for adversaries to detect our transmitters. This vulnerability is particularly concerning in secure communications, as the exposure of device information could potentially result in the compromise of communication content, posing significant security threats. To counteract such risks and safeguard transmitters against unauthorized identification, this paper proposes a novel RFF concealment (RFFC) method based on IQ imbalance compensation and digital pre-distortion (DPD) techniques. This method not only effectively conceals the RFF, preventing malicious detection of the transmitter, but also enhances the system’s linearization performance. The effectiveness of the proposed RFFC framework is validated through MATLAB Simulink and a software and hardware test platform. Experimental results show that using the blind generalized linear structure-based IQ imbalance and deep neural network (DNN)-based PA nonlinearity joint concealment method performs best, reducing transmitter identification accuracy to only 17% under various signal-to-noise ratio conditions. Additionally, this method performs the best in system linearization performance.

Radio frequency fingerprint (RFF) has been widely used in wireless transceivers as an additional physical security layer. Most of the existing RFF extraction methods rely on a large number of labeled signal samples for model training. However, in real communication environments, it is usually necessary to process timely received signal samples, which are limited in quantity and are difficult to obtain labels, the performance of most RFF methods is generally poor. To effectively extract features from the limited and unlabeled signal samples, we propose an efficient RFF extraction method using an asymmetric masked auto-encoder (AMAE). Specifically, we design an asymmetric extractor-decoder, where the extractor is used to learn the latent representation of the masked signals and the decoder as light as a convolution layer reconstructs the unmasked signal from the latent representation. Using commercial off-the-shelf LoRa datasets and WiFi datasets, we show that the proposed AMAE-based RFF extraction method achieves the best performance compared with four advanced unsupervised methods whether in the case of large data size or small data size, or under line of sight (LOS) and non line of sight (NLOS) channel scenarios. The codes of this paper can be downloaded from Github: https://github.com/YZS666/AnEfficient-RFF-Extraction-Method.

Specific emitter identification (SEI) refers to a process to determine the category of emitters by extracting, analyzing and matching the characteristics of received emitter signals. With the increasingly complex environment, traditional SEI methods, such as parameter matching, become difficult to meet the needs of robust and effective signal identification. Deep learning (DL) possesses powerful feature extraction ability and has been widely used in SEI. The superior performance of DL-based SEI methods also brings problems of redundant model parameters and high feature dimensionality, which further causes slow convergence rate, high storage requirements, and ever-increasing computational complexity. In this paper, we propose an SEI method based on end-to-end sparse feature selection (SFS) to make model pay more attention to features with good identification performance. Specifically, we add sparse parameters to features and design loss function composed of cross-entropy loss and sparse regularization. Several experiments are conducted on ADS-B, WiFi and LoRa datasets. From the simulation results, our proposed SFS-SEI method improves feature sparsity, speeds up loss convergence, reduces model parameters on the premise of ensuring accuracy. Code is available at: https://github.com/sleepeach/SFS-SEI.