The increasing proliferation of unmanned aerial vehicles (UAVs) poses significant challenges to airspace security, necessitating the development of effective detection technologies. Passive detection techniques, such as passive radar, offer key advantages including spectrum efficiency and covert operation. However, passive radars that rely on coherent integration are often computationally expensive and dependent on strong Doppler signatures, rendering them ineffective for detecting low-speed or hovering UAVs. To overcome these limitations, we explore the use of channel state information (CSI) time series to characterize UAV presence and propose a temporal variation network for detecting UAV states, including hovering conditions. Our method utilizes digital terrestrial multimedia broadcast (DTMB) signals, which have wide coverage and high transmission power. By capturing DTMB signals with a single receiver, we reduce the complexity of passive detection systems. First, we perform channel estimation on the received signal to obtain CSI, which is arranged in frame order to form a CSI time series. This enables the modeling of interference channels caused by UAVs. We then propose the Channel Estimation and Temporal Variation Network (CETVNet), which leverages an adaptive noise reduction module and a multi-period feature extraction module to process these series for passive UAV state detection. Finally, a real-world signal dataset is collected using a software-defined radio device to train and evaluate CETVNet. Experimental results demonstrate that CETVNet achieves superior performance compared to state-of-the-art methods.

Automatic modulation classification of electromagnetic signals has an important role in the field of signal processing. The current methods for identifying the modulation pattern of radio signals mainly used end-to-end neural network models, which suffered from the problems, such as network redundancy and noninterpretability. In order to solve the above problems, this article proposes a radio signal modulation pattern recognition method based on time-frequency adaptive decomposition and hybrid neural network. The discrete wavelet decomposition is used to decompose the original radio signals into adaptive high and low frequencies. Then, feed the resulting high- and low-frequency portions into a multichannel hybrid neural network (MHNN) for training to obtain the classification results. In the discrete wavelet decomposition process, the number of decomposition layers and the decomposition threshold are determined adaptively according to the difference of signal-to-noise ratios (SNRs). In the design of multipath hybrid neural network structure, the signal characteristics of high- and low-frequency parts are considered comprehensively. The network channels with different convolutional kernel scales and different layers are constructed to reduce the number of model parameters and training time while ensuring the classification accuracy. Using the RadioML2016.10a dataset for experiments, the recognition accuracy of MHNN is prior to the end-to-end deep learning methods, which can achieve the accuracy of 92.30% and the F1 of 91.84% when SNR=2 dB. It shows that the proposed radio signal modulation pattern recognition method based on the time-frequency adaptive decomposition and hybrid neural network can balance the accuracy and lightweight. Moreover, it has certain interpretability for the original input data, distinguishing the main body and details of the signal by dividing the signal into high and low frequencies, which provides a new idea for the interpretability of deep convolutional neural networks.

Signal Modulation Classification (SMC) is a fundamental technique in wireless communications. However, the prevalence of label noise in practical scenarios severely constrains the advancement of SMC technology. Existing SMC methods heavily rely on high-quality labeled data and often underutilize the inherent physical prior knowledge of signals. To address these issues, this article proposes a Physics-Aware Collaborative Framework with Prototype Consistency (PhyCo-PC), designed for noisy label environments and operating without requiring reliable labels. Firstly, the framework leverages co-teaching for noise identification and incorporates a collaborative consensus-guided module for prototype learning and pseudo-label generation. Secondly, it constructs physics-guided downstream decision module that fuses deep learning features with instantaneous physical signal characteristics to enhance decision robustness. Thirdly, a Domain Knowledge-guided Adaptive Sample Selection (DKASS) strategy is introduced. DKASS parameterizes the selection rate scheduling function, incorporates domain knowledge to constrain the search space, and utilizes automated search for optimization. This enables the model to adaptively determine the optimal training strategy for varying noise environments. Finally, experimental results demonstrate that PhyCo-PC significantly improves SMC classification performance under complex label noise scenarios on the RML2016.10a/04c datasets, exhibiting excellent robustness and significant advantages.

This paper conducted a thorough investigation into the primary difficulty of the cross-channel automatic modulation classification (AMC) task by examining data distribution and feature space of different channel conditions. We concluded that the disruption of the target channel feature space structure breakdown the mapping relationship across channels, serving as the main contributor to model performance degradation. Based on the above conclusion, in order to improve the performance of cross-channel AMC, we introduce the Auxiliary Feature Learning-Guided Network (AFLNet). This network improves the structure of the target feature space through two uniquely designed tasks and facilitates efficient cross-domain alignment via a collaborative alignment mechanism. Specifically, AFLNet integrates similarity-based and confidence-based auxiliary feature learning tasks to enhance the discriminability of the target feature space and maintain the correspondence of category structures across different channels, thereby reducing the difficulty of feature alignment. The collaborative alignment mechanism combines adversarial training-based and self-training-based feature alignment methods, leveraging their mutually reinforcing effect and complementary strengths in global alignment and class-level alignment to enhance overall alignment performance. We carried out extensive experiments across four scenarios characterized by substantial channel variations, verifying that AFLNet achieves state-of-the-art with accuracy improvement of up to 9.71%.

Automatic Modulation Classification (AMC)-oriented Deep Neural Networks (ADNNs) have received much attention in recent years for their wide range of applications. However, they are vulnerable to attacks. Adversarial Examples (AEs) of modulation signals with added weak perturbations can easily fool ADNNs. The study of AEs on AMC, on one side, can enhance the security of wireless communication systems; on the other side, it can provide an effective defence against potential attacks. Nevertheless, most existing attack methods generate AEs with low transferability. In this paper, we propose a Multiscale Discriminative Attack Method (MDAM) for modulated signals. The method strives to alleviate such transferability issue by destroying discriminative features in multi-layer. Specifically, we utilize interpretable class activation maps to distinguish the discriminative regions, ignoring the noise and focusing on the interference of the discriminative features. Beyond that, we propose a multi-layer activation disruption loss to constrain activations in the middle layers. In so doing, the AEs do not erroneously retain deep features of the original signal. We conduct extensive experiments on RadioML datasets and the local area network (LAN) communication dataset we collected to evaluate the effectiveness of MDAM in both white-box and black-box attack scenarios. The results show that MDAM outperforms existing methods.

Learning from a limited number of samples in automatic modulation classification (AMC) has garnered considerable attention. However, existing few-shot AMC works solely focus on single-domain conditions where the training and testing data share the same data distribution, which overlook the potential domain differences. In practice, the complex and variable communication channels, along with different radio frequency (RF) devices, may result in significant data distribution differences, which can be defined as cross-domain conditions. The neglect of such cross-domain conditions may leads to a significant decline in the performance of existing few-shot AMC models. To consider a more general situation, this paper unifies single-domain and cross-domain few-shot AMC into one task, named SaC-FSL. We propose the Paired Samples Relationship Network (PSRNet) as a solution. PSRNet does not require additional network structure design for domain shifts. It distinguishes categories by learning the relationships between sample pairs rather than directly learning the features of samples. To achieve this, we randomly pair the samples to construct different relationships between different classes and domains, and learn these relationships through classification task. Extensive experiments conducted on multiple datasets have demonstrated the superiority of our PSRNet, which can achieve considerable improvements in both single-domain and cross-domain conditions.

Silent high-dynamic target recognition using single receiver has garnered significant attention due to its advantages in safety and stealth within military operations. This paper presents an in-depth exploration of a silent high-dynamic target recognition method based on frequency modulation (FM) signals. The proposed method processes the FM signals with a least squares filtering approach and further implements the calculation of Cross-Ambiguity Function (CAF) mapping to achieve target imaging within the CAF spectrum. By capturing FM signals with an antenna array tuned to various orientations, we conducted empirical analysis using actual aircraft in flight as the target for identification. The recognition scheme put forth by our research is capable of precisely determining the velocity of the target and the relative distance parameters between dual base stations, facilitating accurate tracking of targets.

The Specific Emitter Identification (SEI) technology has broad application prospects in the fields of the Internet of Things and cognitive communication and serves as an effective means for device authentication. Most existing SEI methods based on deep learning primarily operate in the real number domain. However, complex numbers naturally represent radio frequency signals, making complex-valued neural network(CVNN) a superior choice for signal representation. To address the limitations of traditional real-valued neural network methods, such as low recognition accuracy and the requirement for a large number of training samples, we propose an SEI method based on CVNN to learn the true RF fingerprint features. In the data preprocessing stage, we employ a signal slicing strategy with a random window step size to enhance data randomness and improve the model’s generalization performance. Given the complex-valued nature of signals, we employ a complex-valued convolutional neural network for feature extraction. Subsequently, we design a feature fusion module based on the complex-valued attention mechanism to eliminate redundant features while preserving radio frequency fingerprint features. Considering the high similarity between emitter signals, we employed a joint loss function based on metric learning that promotes intra-class aggregation and interclass separation. The experimental results demonstrate that the proposed method uncovers the authentic RF fingerprint with high accuracy and robustness under limited training sample conditions.

With the advancement of Internet of Things technology, the need for sophisticated signal modulation classification has intensified, ensuring seamless communication and bolstering security among interconnected devices. In the contemporary complex channel environment, the difficult lies in dealing with a multitude of modulation schemes that exhibit subtle distinctions. Prior knowledge-guided and deep learning methods have complementary strengths in the current context of signal modulation classification. To synthesize the advantages of these two methods, we propose an integrated method of prior knowledge and contrast feature for signal modulation classification, called APFS. APFS integrates prior knowledge from the modulation task with feature information acquired through contrastive learning. Feature extraction guided by prior knowledge accurately captures the key patterns in modulated signals. Contrastive learning reveals the inherent distinctions among various modulation modes by comparing different samples. In the joint feature extraction approach for prior knowledge, each form of prior knowledge is first analyzed independently, and then jointed to extract information from its temporal sequence. The contrast features surpass the constraints of labeling and unearth deeper implicit information. In experiments, we systematically compared the performance of our method with various baselines, as well as combinations of prior knowledge and contrast feature. The results demonstrate the superior performance of our method.

Advances in communication technology have enabled signal modulation classification (SMC) to be widely used in noncooperative identification situations, such as spectrum detection, electronic countermeasures, and target identification. In the face of complex electromagnetic environments and various classification tasks, the class imbalance phenomenon in modulated signal data sets has become a problem that cannot be ignored. For the SMC based on machine learning, the unbalanced training data set will cause the actual decision boundary to shift, thereby reducing the prediction accuracy of minority signals. And for SMC based on deep learning, unbalanced data will lead to distortion of the feature space and affect the extraction of discriminative features. However, the existing modulation classification methods cannot effectively deal with the imbalance problem. This study introduces an oversampling method tailored for modulation signals. Our method balances the data set by synthesizing new samples according to the distribution of signal samples and the distance between samples, which will effectively reduce the impact of the imbalance problem on the classifier. For modulated signals, experimental results show that our method performs better than other oversampling methods. In addition to the SMC task, we test the performance of the proposed method for individual identification of radiation sources on the aircraft communications addressing and reporting system data set. Compared with other comparison methods, our method improves the classification performance the most.

Seamless Internet of Things (IoT) connections expose many vulnerabilities in wireless networks, and IoT devices inevitably face many malicious active attacks. automatic modulation recognition (AMR) is an effective way to combat IoT physical layer threats. In the field of noncollaborative communication, feature representation learning for unlabeled signals is an important task of AMR. However, due to the unavailability of a priori knowledge and the influence of interference during signal transmission, the intercepted unlabeled signals are difficult to perform efficient feature representation. In this article, we propose cooperative contrast learning for unlabeled modulation signal Cooperative Contrast Learning for modulation Signals (CoCL-Sig). Specifically, the CoCL-Sig is trained using both sequence and constellation diagram modalities, and is divided into two parts: 1) modal-level feature representation and 2) instance-level auxiliary feature representation. In modal-level feature representation, two modal projections are matched in the same hyperplane space. To ensure the stability of the feature representation, a sequence auxiliary branch is added to form an instance-level feature representation of the sequence. In addition, the feature representations obtained by the CoCL-Sig can be applied to modulation signals for semi-supervised classification and clustering tasks. We have conducted extensive experiments on two widely used modulation signal data sets, RML2016.10A and RML2016.04C. The results demonstrate the effectiveness of our method in modulation signal feature representation and its superiority compared to other methods.

Orthogonal frequency division multiplexing (OFDM) is regarded as one of the key technologies in wireless communications, particularly in the integration of space and ground networks. Nevertheless, the performance of OFDM communication systems will degrade significantly in complex scenarios, which brings severe challenge to reliable information recovery at the receiver. To address this issue, we propose an intelligent receiving method for OFDM communication based on dual-channel convolutional neural network (DCNet) from the perspective of combining knowledge and data-driven, which introduces the domain knowledge of channel estimation to assist the stability of OFDM signal recovery. The experimental results under various simulation conditions demonstrate that the proposed method can effectively enhance the performance of information recovery in OFDM communication systems.

Due to the uncertainty of non-cooperative communication channels, the received signals often contain various impairment factors, leading to a significant decline in the performance of existing deep learning (DL)-based automatic modulation classification (AMC) models. Several preliminary works utilize domain adaptation (DA) to alleviate this issue, however, they are constrained by singular domain difference factor, whereas in practice, these factors often manifest cumulatively. Therefore, this paper introduce a more realistic task named superimposed DA, where multiple domain difference factors are overlaid, reflecting the cumulative nature of them. We propose the SigDA as a solution framework, which adopts adversarial training to align the data distribution in different domains. Two technical modules, Multi-task based Masked Signal Feature Extractor (M2SFE) and Signal Feature Pyramid Aggregation (SFPA), are innovatively designed in SigDA. M2SFE utilizes mask and reconstruction task to enhance feature extraction and achieves discriminative feature selection through the design of feature mapping layers, while SFPA can solve the problem of inconsistent signal length in superimposed DA and can aggregate the features of signals into the same dimension. We consider and superimpose various typical signal domain difference factors, comprehensive experiments demonstrate that the proposed framework can achieve significant performance improvement in various communication channels.

The recent flourish of deep learning on various tasks is largely accredited to the rich and high-quality labeled data. Nonetheless, collecting sufficient labeled samples is not very practical for many real applications. Few-shot Learning (FSL) provides a promising solution that allows a model to learn the concept of novel classes with a few labeled samples. However, many existing FSL methods are only designed for computer vision tasks and are not suitable for radio signal recognition. This paper calls for a radically different approach to FSL: in contrast to developing a new FSL model, we should focus on transforming the radio signal to be better processed by the state-of-the-art (SOTA) FSL model. We propose Modulated Signal Pre-transformation (MSP), a parameterized radio signal transformation framework that encourages the signals having the same semantics to have similar representations. MSP currently adapts to various SOTA FSL models for signal modulation recognition and can support the mainstream deep learning backbone. Evaluation results show that MSP improves the performance gains for many SOTA FSL models while maintaining flexibility.

Deep learning-based recognition of radio signal modulation has emerged as a current research hotspot with significant practical potential. However, in practical applications, radio modulation signal data acquisition is complicated to obtain, and label samples are costly and time-consuming to meet the data dependence of deep learning. Transfer learning allows pretrained networks to be reused on large-scale datasets, making it a kind of solution for modulation signal recognition in limited data. The method of suppressing small singular values in the feature vector is employed in this paper to realize selective knowledge transfer for modulation signal recognition, while stochastic normalization is employed to replace the batch normalization layer to avoid over-fitting. We tested the stochastic normalized selective knowledge transfer method on the RML2016.10A and RML2016.04C datasets, with an SNR of 6dB signal samples, and found that it can lead to average growth of 15.77% and 10.32% when compared to direct training, and 6.1% and 2.73% when compared to vanilla fine-tuning. In addition, we check up under a variety of SNR conditions to ensure that our method is effective.

In cognitive radio networks, providing accurate recognition of the primary user’s signal is great important for designing the spectrum access strategies. Source number estimation has served as a key fundamental technique to facilitate the signal recognition in the mixed received signals scenario. In this paper, we propose a deep learning based source number estimation method under the single-channel conditions. The architecture of the network is first designed. Then, the received complex signals are reconstructed as in-phase and quadrature (IQ) data in order to adapt to the convolutional neural network for extracting the deep features. Moreover, the cost function that is used to train the proposed network is properly designed by exploring the maximum likelihood function. Supervised training is performed to generate a classifier that can identify the number of sources in the mixtures. The proposed deep leaning-based source number estimation method is tested by experiments on simulation signals and actual signals. The results revealed the effectiveness of the proposed method in solving the problem of single-channel source number estimation compared to the conventional methods.

Synthetic Aperture Radar is superior to optical sensors in that it can identify ships at all hours and on all days. Deep learning-based object detection relies on huge amounts of data, yet SAR ship images are challenging to obtain and label. A few-shot cross-domain transfer learning approach for SAR image ship detection is used in this paper. It is divided into two stages: the first uses a large volume of optical remote sensing ship images as the source domain training detection framework, and the second employs SAR ship images and optical remote sensing ship images to create a few-shot balanced subset fine-tuning detection framework. Use a metric learning-based prediction box classifier instead of a fully connected prediction box classifier. When fine-tuning the whole detection frame using the metric learning-based pre-diction frame classifier, the experiments show that an AP50 of 55.99% can be reached with only 10 SAR ship images.

The multimodal representation of signals can give a lot of information in the field of communication modulation signal recognition. Existing approaches can use some of the modes for classification tasks, but they don't take use of the multimodal characteristics of signals well. The approach of signal modulation recognition suggested in this research is based on reinforcement learning and multimodal features. The feature set is obtained via wavelet transform using the 1D features of the signal and the constellation map features, and then the feature set is filtered using reinforcement learning to get a small subset of features to obtain the training feature set. With a small number of extra channels, the accuracy of recognition can be enhanced when utilizing the filtered feature set to categorize the modulation class of the signal. Experiments on the RML2016 dataset validate the effectiveness of our proposed method and achieve good classification performance, which is a pioneering idea for the signal modulation classification problem.

Hyperspectral imaging (HSI) classification has drawn tremendous attention in the field of Earth observation. In the big data era, explosive growth has occurred in the amount of data obtained by advanced remote sensors. Inevitably, new data classes and refined categories appear continuously, and such data are limited in terms of the timeliness of application. These characteristics motivate us to build an HSI classification model that learns new classifying capability rapidly within a few shots while maintaining good performance on the original classes. To achieve this goal, we propose a linear programming incremental learning classifier (LPILC) that can enable existing deep learning classification models to adapt to new datasets. Specifically, the LPILC learns the new ability by taking advantage of the well-trained classification model within one shot of the new class without any original class data. The entire process requires minimal new class data, computational resources, and time, thereby making LPILC a suitable tool for some time-sensitive applications. Moreover, we utilize the proposed LPILC to implement fine-grained classification via the well-trained original coarse-grained classification model. We demonstrate the success of LPILC with extensive experiments based on three widely used hyperspectral datasets, namely, PaviaU, Indian Pines, and Salinas. The experimental results reveal that the proposed LPILC outperforms state-of-the-art methods under the same data access and computational resource. The LPILC can be integrated into any sophisticated classification model, thereby bringing new insights into incremental learning applied in HSI classification.

Multiple-input–multiple-output (MIMO) technology is one of the most widely used communication technologies. However, with the increasing number of antennas, the complexity of the MIMO wireless communication receiver becomes higher and higher. On the other hand, the complex communication channels also raise up a great challenge to the reliability of the communication receiver system. With the rapid development and wide application of deep learning, it has been applied in the field of communication to solve some problems that are difficult to solve by the traditional methods, and thereby, improves the reliability of communication systems. Inspired by this idea, this article reviewed the signal processing process of the MIMO receiver system from the perspective of system reliability. Based on deep learning, the signal processing modules of the receiver system are jointly optimized, which changes the information recovery process of the traditional receiver and proposes the intelligent receiver for MIMO communication. In order to verify the system reliability of the intelligent receiver, this article analyzes it from the aspects of antenna numbers and channel conditions. The influence of different implementation methods of the intelligent receiver on the system reliability is also analyzed. Simulation results show that the proposed intelligent receiver for the MIMO wireless communication can recover information with a lower bit error rate and higher reliability compared with the traditional receiver under different conditions and antenna configurations.

Unmanned-aerial-vehicles (UAVs)-enabled wireless communication for Internet-of-Things (IoT) applications has attracted increasing attention. This article studies a UAV-assisted data dissemination system, where a rotary-wing UAV is dispatched to disseminate data to terrestrial IoT devices. We target to minimize the completion time via a joint optimization of the UAV trajectory and transmit power, while considering the indispensable constraints which cover the maximum energy budget, speed, transmit power of the UAV, and data requirement for each IoT device. First, we formulate the UAV data dissemination as a completion time minimization problem. To tackle the nonconvex optimization problem, the original problem is transformed into two subproblems: 1) the trajectory optimization and 2) the transmit power optimization, respectively, by introducing auxiliary variables and leveraging the concave-convex procedure. Then, we develop a joint trajectory and transmit power algorithm via tailoring the successive convex approximation and alternating descent method. We further improve the algorithm by maximizing the throughput instead of minimizing the completion time in the transmit power optimization process. The improved algorithm not only reduces the computational complexity but also enhances the achieved performance. In addition, simulation results demonstrate the superior performance of the proposed algorithms under various parameter configurations.