Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are widely employed in current and emerging Internet of Things (IoT) communication systems. However, in practical deployments, IoT communications frequently encounter complex environmental factors such as sparse pilot allocation, non-linear hardware distortion, and interference, which significantly degrade the detection performance of traditional OFDM receivers. To address these challenges, this paper proposes a structured hybrid deep learning (DL) OFDM receiver architecture, termed DCSE-DAR, which integrates model-driven signal processing with data-driven feature learning. The proposed receiver comprises a Dilated Convolution with Squeeze-and-Excitation (DCSE) module for enhanced channel estimation and a Dual-Activation Residual (DAR) network that unfolds the Minimum Mean Square Error (MMSE) principle into a deep iterative structure. Through structured fusion between these modules and a collaborative two-stage training strategy, the proposed architecture enables effective feature extraction and model-guided signal detection while preserving the interpretability of the classical OFDM receiver pipeline. Simulation results show that the DCSE-DAR receiver is superior to the conventional MMSE receiver as well as representative DL-based receivers under various channel models, sparse pilot configuration, nonlinear distortion and interference conditions, showing better robustness and detection accuracy, which verifies its effectiveness and application potential in the design of intelligent receiver in time-varying IoT communication environment.

Specific emitter identification (SEI) is a security authentication technology by utilizing radio frequency fingerprinting (RFF) features. However, current mainstream RFF feature extraction methods based on neural networks (NNs) generally suffer from poor interpretability and difficulties in architecture optimization. To address these issues, we propose a novel convolution-enhanced swin vision transformer (CoSwinVIT) that combines filter characteristics of NN architectures to achieve a uniform spectrum response. Specifically, we treat the NN used in SEI tasks as filters and investigate their filter characteristics by applying Fourier transforms to hidden vectors. Through spectral analysis, we found that different NN architectures exhibit significantly different responses to the signal spectrum. This affects the model’s sensitivity to specific frequency bands of the signal, thereby influencing its accuracy (Acc). Subsequently, by integrating the filtering properties of swin vision transformer (SwinVIT) and convolutional neural network (CNN), we achieve a uniform spectral response design. Finally, to evaluate the performance of the CoSwinVIT architecture, we design both a supervised learning algorithm and a contrastive learning-based self-supervised algorithm. Experimental results on a real-world automatic dependent surveillance-broadcast (ADS-B) and wireless fidelity (WiFi) dataset indicate that CoSwinVIT provides a more uniform spectrum response. Under supervised learning, the proposed CoSwinVIT obtains the accuracies of 98.6% and 99.8% on the ADS-B and WiFi datasets, respectively. Under self-supervised learning with 5% and 10% labeled data, the accuracies of 73.6% and 86.3% are achieved on the ADS-B dataset, and the accuracies of 81.6% and 91.5% are achieved on the WiFi dataset. These results surpass the state-of-the-art (SOTA) methods used in the SEI tasks.

The broadcast nature of device authentication systems in the Internet of Things (IoT) and maritime transportation makes real-time signals susceptible to forgery attacks, leading to critical risks, such as information leakage and navigation failures. specific emitter identification (SEI) plays a crucial role in protecting communication systems from spoofing and tampering. However, traditional SEI networks struggle with limited generalizability, high-computational costs, and susceptibility to extraction and inversion attacks. To overcome these challenges, this article introduces sliding window linear attention and the convolution mechanism (SWLC)-Conformer, a novel hybrid SEI architecture that seamlessly integrates SWLC. The proposed architecture dynamically captures both local signal characteristics and global temporal dependencies through a hybrid design incorporating window-based attention blocks and depth-wise convolution layers. Furthermore, to strengthen system security against model inversion threats, we incorporate a perturbation mechanism that employs reverse sigmoid activation in the final feature representation. Experimental results demonstrate that SWLC-Conformer achieves high recognition accuracy, reaching 91.10% on the AIS100 dataset and 97.89% on the WiSig dataset, while maintaining FLOPs at 22.17M and 11.11M, respectively. In terms of security, SWLC-Conformer reduces the accuracy of stolen models by over 40% when the stolen dataset ratio is 0.9. This result highlights the effectiveness of the introduced defense mechanism in degrading the usability of stolen models through controlled feature obfuscation.

Specific Emitter Identification (SEI) is a non-encrypted authentication technology that adds a layer of security to wireless communications in intelligent transportation systems. However, most existing SEI methods are constrained to identify fixed classes and cannot learn incrementally. In practical applications, the constant emergence of new classes or tasks necessitates the capacity to continuously learn new classes from streaming data. In this paper, combining momentum-based prototype correction and hierarchical regularization, a simple and effective non-example class-incremental method for SEI is proposed, named MoPC-HR. The momentum-based prototype correction dynamically adjusts old class prototypes (i.e., old class centers), strengthening the separation between old and new classes and enabling smoother integration of new classes. Hierarchical regularization is applied at multiple levels to control feature differences between old and new classes, preventing the model from favoring new classes and reducing catastrophic forgetting. Experiments on real-world AIS-100 and ADS-B datasets show that MoPC-HR outperforms state-of-the-art methods. Specifically, in the incremental phase 20, MoPC-HR achieves an average accuracy of 97.04%, average forgetting of 2.95%, and average intransigence of 2.85% on the AIS-100 dataset; and an average accuracy of 95.99%, average forgetting of 3.66%, and average intransigence of 3.60% on the ADS-B dataset. The code is available at https://github.com/xmuLdz/MoPC-HR.git

Specific emitter identification (SEI) is a non-cryptographic authentication technique to provide an extra security layer for wireless devices, which has promising applications. However, the traditional methods of SEI are only available in limited equipments. In actual application scenarios, new devices (as new classes) are constantly appearing. In this paper, an effective class incremental learning (CIL) method is proposed for SEI, named class-incremental with self-training and prototype augmentation (CISP). It is a teacher-student network. Firstly, the teacher network trained by the old-class data is utilized to instruct the student network to adapt the new classes while retaining the old-class knowledge through the knowledge distillation (KD) techniques. Secondly, in order to mitigate the problem of favoring the new classes, weight aligning (WA) method is introduced to balance the weights of the new-class and old-class classification layers in the student network. Lastly, the old-class samples are recalled from the unlabeled dataset by the student network and input into the teacher network. Then the feature prototypes of the old classes are constructed and augmented. This would further ease the imbalance between the old and new classes and alleviate the problem of noisy pseudo-labels. Experiment results on the real AIS-100 dataset and ADS-B-100 dataset with the number of the initial classes being 20 and 20 classes per incremental step demonstrate that the proposed method can achieve an average accuracy of 95.29% and 95.84%, respectively. It effectively mitigates the catastrophic forgetting of the model and is superior to the state-of-the-art incremental learning approaches of not saving the old-class samples.

The automatic identification system (AIS) is the automatic tracking system for automatic traffic control and collision avoidance services, which plays an important role in maritime traffic safety. However, it faces a possible security threat when the maritime mobile service identity (MMSI) that specifies the vessels’ identity in AIS is illegally counterfeited. To guarantee the communication security of AIS for preventing fraudulent devices, we design a novel lightweight Transformer-based network GLFormer for specific emitter identification (SEI) to provide an extra security layer for AIS terminal emitters. Concretely, the gated local attention unit (GLAU) and the gated sliding local attention unit (GSLAU) modules that combine a simplified gated attention unit (GAU) and a sliding local self-attention (SLA) are developed in GLFormer to extract the radio frequency fingerprint (RFF) features automatically from the raw in-phase signals. Especially, the simplified GAU focuses on more critical RFF features and filters out the irrelevant information from the raw signal to improve performance, which is also a single-head self-attention module with fewer parameters for lightweight. Meanwhile, the SLA limits self-attention operation to a window, introducing the inductive bias of local information to enhance performance further and reducing the quadratic computational complexity to linearity for efficiency. Experimental results demonstrate that the GLFormer achieves 96.31% and 89.38% identification accuracy in the constructed AIS transient and AIS steady-state datasets with 50 vessels, respectively. The 99.90% identification accuracy is achieved in the universal software radio peripheral (USRP) dataset with ten devices. It is not only better than the existing methods but requires much fewer parameters and lower computational complexity; besides, it is also suitable for working with long signal sequences.

In this letter, a novel relocating enhanced nested array (RENA) configuration is proposed. Compared with most existing sparse array configurations, the proposed RENA has a hole-free difference co-array, simple closed expressions for the array geometry and degrees of freedom (DOFs), and also achieves more consecutive DOFs. Based on the above good properties of the proposed RENA, we improve a root multi-signal classification algorithm based on pseudo-noise subspace (PNS-root-MUSIC) for direction of arrival (DOA) estimation. The PNS-root-MUSIC algorithm has lower algorithm complexity due to no exhaustive spectral peak search, and takes full advantage of the larger hole-free co-array of the proposed RENA, yielding a higher accuracy of DOA estimation. The results of theoretical analysis and simulations demonstrate the superior performance of the proposed RENA. The simulation results show that the improved PNS-root-MUSIC algorithm has better DOA estimation performance compared with that of existing algorithms.

Illegally forged signals in automatic identification system (AIS) pose a threat to maritime traffic safety management. In this paper, a multi-level sparse representation based identification (MSRI) algorithm is proposed for specific emitter identification (SEI) in the AIS. The MSRI innovatively combines neural networks with sparse representation based classification (SRC). Channel attention mechanism is introduced to a multi-scale convolutional neural network (CNN) for extracting hidden features in the signal. These extracted features are divided into shallow and deep features according to the depth of the network layer they are extracted from. The original AIS signals and the two-level features are spliced together to form a multi-level dictionary. Subsequently, a sparse representation based identification is performed on the decorrelated multi-level dictionary using the principal components analysis (PCA) method. The proposed MSRI is evaluated on a dataset composed of real-world AIS signals, and compared with the state-of-the-art identification algorithms. The evaluation is based on several factors including computational complexity, number of training samples, and number of emitters. Numerical results indicate that the proposed algorithm can identify emitters with higher accuracy and requires lower training time compared to other methods. Given more than 15 training samples at each emitter, the MSRI can identify nine emitters with an accuracy higher than 90%.

The acoustic signal transmission over the underwater channel has a limited sum rate and it consumes high power due to the properties of the underwater environment. This study attempts to use the non‐orthogonal multiple access (NOMA) technologies for underwater communications. NOMA can be an attractive candidate for underwater communication due to its high spectral efficiency, resistance for carrier frequency offset, and efficient energy consumption. To cope with the hard‐recharging capability of the underwater wireless sensor nodes caused by the ocean environment, this study proposes a novel transmission scheme called time‐reversed NOMA (TR‐NOMA) for underwater communication. In the proposed TR‐NOMA, a single‐input multiple‐output NOMA scheme with a passive‐time reversal technique is proposed to reduce the time–frequency dispersion of the underwater acoustic channels. Consequently, simultaneous wireless information and power transfer (SWIPT) can be applied for underwater TR‐NOMA. In this study, a SWIPT‐NOMA is postposed to harvest energy in downlink transmission from the transmitted signal. The bit error rate (BER) and the outage probability are used to characterise the performance of the proposed TR‐NOMA scheme and simulation results show how the proposed TR‐NOMA significantly outperforms the conventional NOMA schemes. Additionally, a mathematical framework for the average BER of TR‐NOMA is delineated.

The novel time-frequency transform, called synchro-compensating chirplet transform, is proposed to represent optimally the overlapping signals in a time-frequency domain. By the control of the self-tuning demodulated operator, an instantaneous rotating operator is introduced in the proposed algorithm to blur the noise and compensate the energy of each component simultaneously. The effectiveness of the method for micro-Doppler signals with a high noise is validated through examples.