Radio Frequency Fingerprint Identification (RFFI) has emerged as a promising technique for enhancing wireless security by uniquely identifying individual devices through their inherent RF characteristics. However, the performance of conventional RFFI methods deteriorates significantly when training and testing involve different modulation schemes, primarily due to the resulting domain shift between modulation types. To address this challenge, this paper proposes Domain-Invariant Adaptive Mixup Enhancement (DIAME), a novel framework that integrates domain-invariant feature extraction with a similarity-aware adaptive mixup strategy to improve generalization across modulation domains. Specifically, DIAME dynamically adjusts the mixup intensity based on inter-feature similarity and incorporates domain alignment and feature matching with pretrained models to promote modulation-invariant representation learning. Extensive experiments on a synthetic RF dataset comprising four modulation types and five devices demonstrate that DIAME achieves an average cross-modulation identification accuracy of 86.18%, significantly outperforming state-of-the-art methods. These results confirm the effectiveness of DIAME in mitigating domain shift and highlight its suitability for robust RFFI in heterogeneous wireless communication environments.

With the growing adoption of Internet of Things (IoT) devices, ensuring the security of wireless communications has become increasingly critical. Radio frequency fingerprint identification (RFFI) has shown promise in this regard due to its capability of uniquely identifying devices. Although deep learning (DL) approaches have significantly improved RFFI performance, they typically rely on large-scale centralized data. This poses challenges in terms of privacy preservation and heterogeneous data distributions. To address the performance degradation caused by non-independent and identically distributed (non-IID) data in cross-receiver scenarios, this paper proposes a feature alignment strategy based on federated learning (FL) for RFFI. In such scenarios, due to differences in receiver hardware characteristics, deployment locations, and channel conditions, the signals captured by different receivers often exhibit distribution shifts, resulting in misaligned feature spaces across clients. The proposed method guides each client to learn aligned intermediate feature representations during local training, effectively mitigating the resulting adverse impact on model generalization. Experiments conducted on a real-world RF dataset demonstrate that the proposed method achieves higher identification accuracy and improved stability compared with representative federated baselines, including FedAvg and FedProx. The highest identification accuracy reaches 90.83%, and the performance gains are accompanied by generally reduced variance across different client configurations, highlighting the robustness and generalization capability of the proposed approach in heterogeneous wireless environments.

Specific Emitter Identification (SEI) leverages unique hardware-induced Radio Frequency Fingerprints (RFFs) for secure physical-layer authentication. However, under cross-receiver scenarios where training and testing data exhibit hardware-induced distribution shifts, deep learning models are prone to shortcut learning. In such cases, networks inadvertently exploit spurious, receiver-specific artifacts as ”shortcuts” for identification rather than extracting the genuine, intrinsic fingerprints of the transmitter. To overcome this challenge, we propose a robust multi-task learning framework, termed MTL-SEI. This framework synergizes spectrum-based feature extraction with receiver-invariant adversarial training and channel-aware auxiliary supervision. Specifically, a gradient reversal layer (GRL) is employed to suppress receiver-dependent features, while an equalization-state prediction task provides semantic guidance to disentangle channel-induced distortions. Furthermore, an uncertainty-guided task weighting mechanism is introduced to dynamically balance the multiple optimization objectives based on predictive variance. Evaluations conducted on the ManySig dataset under a rigorous receiver-disjoint protocol demonstrate the superior generalization capability of MTL-SEI. Notably, our method achieves a transmitter identification accuracy of 88.50% —representing a 37.7% improvement over the 1D-CNN baseline—and yields an average performance gain of over 6.92% compared to state-of-the-art domain generalization methods. These results validate the effectiveness of the proposed feature disentanglement mechanism in mitigating receiver-induced biases.

With unmanned aerial vehicles (UAVs) widely applied in diverse fields, their potential safety risks are more prominent. Accurate UAV identification is crucial. This paper presents a method combining wavelet decomposition and the channel-enhanced attention mechanism. Two-dimensional discrete wavelet transform (2D-DWT) analyzes UAV radio frequency (RF) signal spectrograms for multi-resolution, extracting key information while decomposition data volume and computational complexity. The efficient channel attention (ECA) mechanism boosts the model’s expressiveness. Together with attention-based multi-scale convolution network (AMSCNet), it extracts multi-scale features, reducing information loss and enhancing identification. Experimental results show an average accuracy of 97.00%, outperforming residual network (ResNet) and efficient neural network (EfficientNet). It also has low computational complexity and stable performance across various scenarios, offering an efficient and reliable UAV identification solution.

With the rapid advancement of wireless communication technologies, WiFi signals have become essential for modern connectivity across diverse applications. However, their widespread deployment introduces significant security vulnerabilities, including unauthorized access, data leakage, and interference. Accurate identification of WiFi transmitters is crucial for mitigating these threats. While existing methods perform well in ideal conditions, their effectiveness degrades in real-world scenarios, particularly in environments with low signal-to-noise ratios (SNRs). To address this limitation, we propose a novel transmitter identification framework that integrates blueprint separable convolution (BSC) and a squeeze-and-excitation (SE) module. The BSC extracts critical features efficiently, while the SE module dynamically enhances feature representations. Simulation results demonstrate that the proposed approach achieves competitive or superior accuracy compared to state-of-the-art models. Moreover, the framework exhibits strong robustness, maintaining high recognition performance even in challenging transmission conditions with low SNRs.

Specific emitter identification (SEI) is a crucial technique for recognizing individual emitters based on the unique characteristics of their radio frequency signals. Deep learning (DL)-based SEI has become the dominant method for identifying and authenticating wireless devices. However, in real-world applications, electromagnetic signals are subject to dynamic channel conditions and variations across different receivers, leading to significant performance degradation when models trained on specific datasets are applied to new, unseen environments. This variability challenges traditional DL methods, making domain generalization (DG) an essential approach to tackle this issue. In this paper, we propose a robust SEI method, latent distribution mixing (LDM), to enhance model generalization in receiver-agnostic scenarios. Our approach first applies the Fourier transform to convert time-domain signals into frequency-domain representations. Then, it mixes latent feature distributions across domains in the feature space to improve robustness, enabling the model to adapt to domain shifts effectively. We evaluated our method on a cross-receiver dataset, achieving a peak performance of 88.52%, surpassing other domain generalization methods. The experimental results demonstrate that the proposed LDM method offers a promising solution for SEI tasks in cross-receiver scenarios. Our code can be downloaded from https://github.com/frownean/LDM.

The widespread use of unmanned aerial vehicles brings both convenience and potential security risks, posing significant challenges for accurate UAV identification. To address the limitations of existing deep learning-based radio frequency fingerprinting methods in terms of computational complexity and model adaptability, this paper proposes an innovative hybrid model that combines Convolutional Neural Networks and Transformers to exploit both local and global features of RF signals fully. Our model consists of a Local Block and a Global Block. The Local Block employs Partial Convolution for feature extraction, PointWise Convolution to enhance feature representation, and the Squeeze-and-Excitation module to adaptively emphasize critical features, thereby improving local feature expressiveness. The Global Block comprises Unfold, Super Token Transformer Block, and Fold, which together enable effective modeling of global dependencies through signal unfolding, spatiotemporal transformations, and reconstruction. Experimental results show that under signal-to-noise ratio (SNR) conditions ranging from –5 dB to 20 dB, our method achieves an average recognition accuracy of 96.89%, with powerful performance under low SNR conditions. Furthermore, experiments demonstrate the model’s robustness with limited data. Even with only 1,000 training samples, the model maintains an accuracy of 92.14%. When incorporating Mixup data augmentation, high classification performance is sustained with just 500 training samples. These results highlight the method’s strong adaptability and practical potential in complex environments. Our codes and models are available at https://github.com/Zhoukaijie-hy/hybrid-model.

Radio frequency fingerprint identification (RFFI) is regarded as one of the most promising techniques for managing and regulating Internet of Things (IoT) devices. This technology analyzes the unique electromagnetic signals emitted by wireless devices to enable precise identification and authentication. Most existing RFFI methods focus on RF signals collected in specific scenarios. However, in real-world applications, signals are often collected at different times or from varying deployment locations, leading to differences between the training and testing distributions. The study of RFFI methods under these conditions remains underexplored. To address this gap, this paper introduces a cross-domain RFFI framework centered on adaptive semantic augmentation (ASA). The framework integrates a computationally efficient multi-resolution spectrogram decomposition strategy with a feature-sensitive multi-scale network. The ASA method enhances RFFI accuracy in cross-domain settings by linearly interpolating between two distinct semantic features to create new semantics for further identification. The proposed approach leverages two-dimensional discrete wavelet transform (2D-DWT) to decompose the raw spectrogram into four sub-bands, followed by a multi-scale network to extract critical semantic features for the ASA method. Simulation results show that the proposed ASA method significantly improves Unmanned Aerial Vehicle (UAV) identification performance, achieving accuracies of 93.05% and 98.90% on two different cross-domain datasets, respectively, outperforming existing data augmentation (DA) methods. Furthermore, generalizability validation demonstrates that the proposed method performs outstandingly across other Internet of Things (IoT) applications.

As unmanned aerial vehicles (UAVs) become increasingly prevalent in various environments, their detection is essential for ensuring safety and effective management. For non-standard transmitter waveforms, identifying a specific model poses challenges, and most researchers focus on developing deeper and broader architectures to improve performance at the expense of computational burden and speed, which is not suitable for the deployment of UAV radio frequency (RF) fingerprinting identification. In this paper, we propose a novel multi-resolution analysis-based method for UAV identification. We develop a lightweight, multi-scale convolutional network that utilizes various receptive fields to extract unique hardware intrinsic features. We employ the two-dimensional Discrete Wavelet Transform (2D-DWT) to innovatively decompose the low-frequency and the high-frequency components of the RF signal spectrogram into four distinct sub-bands. Simulation results indicate that our proposed methods outperforms other state-of-the-art UAV identification methods in terms of both performance and computational complexity.

Specific emitter identification(SEI) plays an integral role in network security. In recent years, deep neural networks (DNNs) have demonstrated significant success in various application scenarios. The robust feature extraction capabilities of DNNs have led to advancements in SEI. However, it has been shown that DNNs are susceptible to adversarial attacks. The proposal of well-performing adversarial attacks is conducive to improving the security of SEI with DNN-based models. This paper introduces an universal black-box adversarial attack algorithm, named UBBA, for SEI with DNN-based models. The experimental findings indicate that this universal black-box adversarial attack algorithm substantially reduces the identification accuracy of SEI models. Given a sufficient number of queries, the proposed algorithm achieves an attack effect similar to that of the universal adversarial perturbations (UAP), a universal white-box attack algorithm. Additionally, the results demonstrate that when the perturbation signal is not synchronized with the signal under attack, the proposed algorithm outperforms the fast gradient sign method (FGSM).

Specific emitter identification (SEI) has emerged as a notable device authentication technology, distinguishing various emitters through the unique radio frequency fingerprint (RFF) inherent in wireless devices. Traditional SEI methods, often hindered by time-consuming manual feature extraction, struggle with complex encrypted signals. The advent of deep learning, with its robust feature extraction capabilities, has significantly advanced SEI, yet it typically demands extensive radio frequency signal samples and falters with limited (i.e., few-shot) samples. Our proposed few-shot SEI (FS-SEI) approach, integrating neural architecture search (NAS) and deep transfer learning (DTL), adeptly identifies few-shot long range (LoRa) devices. This method begins with NAS to autonomously tailor optimal network architectures for SEI tasks, followed by pre-training on extensive auxiliary datasets to extract general RFF features of LoRa devices. Transfer learning then fine-tunes these features for distinctiveness with compact intra-class distances. By only utilizing few-shot LoRa data for final parameter adjustments, the classifier rapidly assimilates new categories. Simulations confirm our FS-SEI method's superior accuracy over classical approaches, with visualized feature analysis underscoring its distinguishing and generalizing prowess.

The inherent flexibility of small unmanned aerial vehicles (UAVs) enables their deployment across various emerging markets. Unauthenticated UAVs pose a significant threat if they intrude into aviation-sensitive areas. To address this issue, deep learning (DL)-based radio frequency fingerprint identification (RFFI) has been developed as a promising approach for identifying illegal UAVs. However, these commonly used DL-based methods demand high computation and storage requirements, which are not suitable for the deployment of RFFI. In this paper, we propose an efficient and low-complexity RFFI method for UAV identification. Specifically, we design a lightweight backbone network consisting of lightweight multi-scale convolution (LMSC) blocks that can significantly reduce the model size and enhance the feature extraction ability. The simulation results indicate that our proposed UAV RFFI method outperforms other state-of-the-art and popular DL-based RFFI methods in terms of both identification performance and complexity. The identification accuracy surpasses that of all other methods at low signal-to-noise ratios (SNRs) and achieves nearly 100% accuracy at high SNRs. To further enhance model efficiency, we employ data truncation in our experimental simulations, demonstrating that a sample length of 2000 is sufficient to retain high identification performance. Additionally, we incorporate the Mixup regularization strategy, which improves accuracy without increasing the complexity, especially as sample length decreases.