Automatic modulation classification (AMC) is a key technique for identifying the modulation schemes of wireless signals, enabling improved performance and security in communication systems by accurately classifying signal types. However, most existing AMC research assumes modulation classes are part of a closed set, which can cause classifiers to misidentify unknown modulation schemes as known ones, undermining both the security and reliability of communication systems. To address this, we propose a novel open set AMC (OS-AMC) method based on deep metric learning and OpenMax (M-OpenMax). The proposed M-OpenMax-based OS-AMC method utilizes crossentropy loss and center loss to extract separable and discriminative signal features and uses OpenMax to adjust the nonnormalized score output of the model to achieve the classification of known signals and removal of unknown signals. Experimental results demonstrate that the proposed M-OpenMax-based OSAMC method outperforms other open-set AMC techniques, particularly in its ability to handle unknown modulation types.

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.

Automatic modulation classification (AMC) is a fundamental technology for identifying modulation types in non-cooperative communication systems. It plays a crucial role in various applications, including spectrum monitoring, cognitive radio, and signal intelligence. Recently, deep learning (DL) based AMC methods have achieved remarkable classification accuracy. However, their practical deployment in resource-constrained edge devices remains challenging due to their high computational complexity and excessive model size. To address this limitation, we propose an ultra-lightweight AMC method based on filter pruning via geometric median (FPGM). The key idea is to leverage the geometric median as a robustness-driven filter selection criterion, effectively eliminating redundant convolutional kernels while preserving essential model representations. Specifically, we first determine the geometric median of the filters in each layer, which effectively represents the distribution of filters within that layer. Then, filters near the geometric median are identified and filtered out through the characteristics of the geometric median. Finally, the performance degradation of the model caused by the removal of filters can be restored through fine-tuning. Experimental results demonstrate that the proposed AMC method achieves a 99% reduction in model size while limiting the classification accuracy drop to merely 1.61%, significantly outperforming other lightweight AMC techniques. These results highlight the feasibility of deploying the proposed AMC model on edge Internet of Things (IoT) devices, enabling efficient real-time modulation classification with minimal computational overhead.

Network intrusion detection (NID) is a critical area of research in network security. While deep learning based NID methods have recently achieved advanced detection performance, they often struggle with limited labeled traffic and the resource constraints of edge Internet of Things (IoT) devices. To address these challenges, we propose an advanced few-shot NID method using lightweight transfer learning (LTL), termed NID-LTL. Our approach begins by pre-training a detection model on the large-scale auxiliary dataset to learn universal representations of network traffic characteristics. Then, an automatic pruning strategy is crafted to prune the pre-trained model, which uses a kernel based nonlinear traffic feature selection algorithm to filter out the key information most relevant to the original traffic. Finally, the layer-wise knowledge distillation method is combined to transfer the useful knowledge learned by the pre-trained model to a lightweight student model. This method can not only quickly adapt to novel few-shot NID tasks, but also further compress the model size, reduce computational and storage overhead. Experimental results demonstrate that the proposed NID-LTL method has excellent classification performance with small model sizes, low parameter counts, and low floating point operations (FLOPs). Especially, in the 1-shot scenario, the NID-LTL method achieves 89.38% classification accuracy with only 1.41% of the parameters in the original model.

Malware traffic classification (MTC) is a crucial step in network intrusion detection, which is significant for network security and management. With the continuous evolution of malware traffic, traditional MTC methods are difficult to adapt efficiently to new traffic categories, and manually designed neural network structures suffer from performance bottlenecks and low design efficiency. Hence, we propose an enhanced MTC method based on expandable class incremental learning (CIL) with architecture search. The architecture search can automatically design the optimal neural network structure tailored to different network traffic characteristics, avoiding the limitations of manually designing network structures and improving classification performance. Meanwhile, expandable CIL allows the MTC model to gradually learn new traffic categories without forgetting previous knowledge, avoiding the computational overhead and efficiency loss caused by frequent retraining of the model. The experimental results demonstrate that the proposed CIL-MTC approach surpasses advanced incremental learning methods on both the Edge-IIoTset and ISCX VPN-nonVPN datasets, achieving superior classification performance while maintaining lower average trainable parameters and training costs. Especially, it achieves an average incremental accuracy of 98.55% and 99.09% on the Edge-IIoTset dataset with incremental tasks of 5 and 2, respectively.

Automatic modulation classification (AMC) plays an important role in the field of physical layer security, providing a new way to enhance the security of data transmission and anti-interference ability. Recently, deep learning (DL) has been widely applied in radar and communication signal classification, which requires sufficient labeled training samples to achieve high classification accuracy. However, in non-cooperative situations, it is difficult to obtain a large number of labeled signal samples. Therefore, we propose a novel few-shot AMC method using architecture search and knowledge transfer. This method first utilizes the state-of-the-art neural architecture search algorithm, A-DARTS, to automatically search for the optimal network structure (i.e., Auto-MCNet) based on the auxiliary sample set. Then, the Auto-MCNet model is pre-trained on the auxiliary dataset to explore prior knowledge about signal classification. Finally, we transfer this knowledge to a few-shot training dataset and fine-tune the Auto-MCNet model to enhance its generalization ability. The simulation results show that compared to advanced competitors, Auto-MCNet achieves higher classification accuracy with lower model complexity.

Malware traffic classification (MTC) plays an important role in cyber security and network resource management for the secure Internet of Things (IoT). Many deep learning (DL)-based MTC methods have been proposed due to their robustness and effectiveness with self-designed model architecture. However, to completely adjust complex parameters in the DL model, the architecture design of the DL model requires substantial professional knowledge and effort from human experts. To solve these problems, we propose an automatic and efficient MTC method using neural architecture search via proximal iterations (NASP), which can automatically and efficiently search the optimal model architecture according to the network traffic in the realistic environment. Specifically, we first describe NAS as a constrained optimization problem by keeping the search space differentiable and forcing the architecture to be discrete in the search process. Second, a suitable regularizer is introduced to balance the complexity and performance of the model architecture. Finally, the simulation results show that the proposed NASP-aided MTC method not only can efficiently and accurately search the optimal classification model architecture on the USTC-TFC2016 data set and the Egde-IIoTset data set but also compared with the typical MTC methods it can achieve the optimal classification performance with the fewer parameters as well as the floating-point operations (FLOPs).

The implementation of Scenario Classification (SC) plays a pivotal role in various edge intelligence applications, notably in fields such as autonomous driving, navigation, and remote sensing. With recent advancements, deep learning (DL) techniques have substantially improved SC, delivering remarkable results in classification tasks. However, the integration of DL in SC brings significant computational demands, posing challenges for deployment on edge devices where resources are constrained. Addressing this issue, we propose a novel Low-Resource Scenario Classification (LR-SC) approach, primarily focused on model pruning. This strategy aims to reduce computational power and storage needs, thus optimizing resource utilization in edge intelligence applications. Our approach involves the application of an ℓ2 regularization and a threshold-based pruning method, which selectively eliminates non-essential connections. This is followed by a systematic process of alternating pruning and fine-tuning to mitigate any performance loss due to the pruning. Experimental evaluations of the LR-SC method have shown its effectiveness; it substantially lowers the parameter count to merely 24% of the original model, while simultaneously achieving a 0.42% increase in classification accuracy.

Malware traffic classification (MTC) is one of the important techniques to ensure the security of cyberspace, which aims to detect anomalies and classify different types of network traffic. Recently, MTC methods based on deep learning (DL) have shown their excellent performance. However, these DL-based methods rely on datasets with manually labeled samples for training, which are costly and hard to obtain. To address this problem, this paper proposes a novel self-supervised MTC method based on the framework of masked auto-encoder (MAE). Specifically, MAE first constructs a reasonable unsupervised pretext task with a random masking strategy, which reduces the redundant information in samples and speeds up the pre-training process. The transformer-based backbone network then efficiently extracts features from the non-redundant traffic data efficiently. The proposed MTC-MAE method employs self-supervised learning on a large-scale unlabeled dataset to acquire unbiased features, and fine-tunes on specific datasets to adapt to diverse traffic classification scenarios. Simulation experiments show that our proposed MTC-MAE method is able to learn universal features with high quality and has excellent classification performance on various downstream datasets. The datasets we used, code implementation, and pre-trained models are available on GitHub.

Ensuring network security, effective malware detection is of paramount importance. Traditional methods often struggle to accurately learn and process the characteristics of network traffic data, and must balance rapid processing with retaining memory for previously encountered malware categories as new ones emerge. To tackle these challenges, we propose a cutting-edge approach using self-paced class incremental learning (SPCIL). This method harnesses network traffic data for enhanced class incremental learning (CIL). A pivotal technique in deep learning, CIL facilitates the integration of new malware classes while preserving recognition of prior categories. The unique loss function in our SPCIL-driven malware detection combines sparse pairwise loss with sparse loss, striking an optimal balance between model simplicity and accuracy. Experimental results reveal that SPCIL proficiently identifies both existing and emerging malware classes, adeptly addressing catastrophic forgetting. In comparison to other incremental learning approaches, SPCIL stands out in performance and efficiency. It operates with a minimal model parameter count (8.35 million) and in increments of 2, 4, and 5, achieves impressive accuracy rates of 89.61%, 94.74%, and 97.21% respectively, underscoring its effectiveness and operational efficiency.

Automatic modulation classification (AMC) holds a significant position in physical-layer security, offering an innovative method to enhance the security of data transmission and anti-interference ability. Recently, deep learning (DL) has seen extensive application in radar and communication signal classification, which requires sufficient labeled training data to ensure great classification performance. However, obtaining a significant amount of labeled samples is extremely challenging in complex and ever-changing electromagnetic environments. Therefore, we propose a novel few-shot AMC method using architecture search and knowledge transfer. This method first utilizes an advanced neural architecture search algorithm,$\Lambda $-DARTS, to automatically search for the optimal network structure (i.e., Auto-MCNet) based on the auxiliary sample set. Then, the Auto-MCNet model is pretrained on the auxiliary data set to explore prior knowledge about signal classification. Finally, we transfer the knowledge to a few-shot training data set and fine-tune the Auto-MCNet model to enhance its generalization ability. The simulation results indicate that when the signal-to-noise ratio (SNR) is greater than 0 dB and the shot of each class is 3 and 10, the average accuracy of the proposed Auto-MCNet is higher than 81% and 90%, respectively. Moreover, compared to advanced competitors, Auto-MCNet achieves higher classification performance with lower model complexity.

Malware traffic classification (MTC) is one of the important research topics in the field of cyber security. Existing MTC methods based on deep learning have been developed based on the assumption of enough high-quality samples and powerful computing resources. However, both are hard to obtain in real applications especially in availability of IoT. In this paper, we propose a few-shot MTC (FS-MTC) method combining knowledge transfer and neural architecture search (i.e. NAS-based FS-MTC) with limited training samples as well as acceptable computational resources, in order to mitigate the identified challenges. Specifically, our proposed method first converts the raw network traffic into traffic images through data pre-processing to serve as input data for the neural network. Second, we use neural architecture search to adaptively search for the effective feature extraction model on the source domain (including Edge-IIoTset, Bot-IoT, and benign USTC-TFC2016). Third, the searched model is pre-trained on source task to achieve the generic feature representation of malware traffic. Finally, we only use few-shot malware traffic samples to fine-tune the pre-trained model to quickly adapt to new types of MTC tasks in realistic network environments. The experimental results show that the proposed NAS-based FS-MTC method has great scalability and classification performance in different FS-MTC tasks, including 5-wayK-shot USTC-TFC2016 dataset and 10-wayK-shot CIC-IoT dataset. Compared with state-of-the-art methods in the field of malware classification, the proposed NAS-based FS-MTC has higher classification accuracy. Especially in the 1-shot case of the USTC-TFC2016 dataset, its average accuracy is as high as 86.91%.

Specific emitter identification (SEI) extracts the fingerprint characteristics of emitters according to the subtle differences of transmitted signals, to distinguish different emitter individuals and prevent unauthorized network access. Deep learning (DL) based SEI methods have been proposed to achieve a good identification performance in recent years. However, these methods highly rely on expert experience to design network structures. These hand-designed fixed network structures lack flexibility, which often leads to insufficient model generalization. Neural architecture search (NAS) can be seen as a subdomain of automatic machine learning (AutoML), which can automatically adjust network structure and parameters according to a specific task. In this paper, we propose a neural architecture search-based SEI method, which can achieve an efficient search of the architecture with the use of a gradient descent algorithm. Experimental results show that the proposed NASEI method both improves the accuracy and reduces the parameter quantity when compared with state-of-the-art methods. Code available at https://github.com/huangyuxuan11/NASEI.git.

Wi-Fi-based passive sensing is considered as one of the promising sensing techniques in advanced wireless communication systems due to its wide applications and low deployment cost. However, existing methods are faced with the challenges of low sensing accuracy, high computational complexity, and weak model robustness. To solve these problems, we first propose a robust channel state information (CSI)-based Wi-Fi passive sensing method using attention mechanism deep learning (DL). The proposed method is called as convolutional neural network (CNN)-ABLSTM, a combination of CNNs and attention-based bi-directional long short-term memory (LSTM). Specifically, CSI-based Wi-Fi passive sensing is devised to achieve the high precision of human activity recognition (HAR) due to the fine-grained characteristics of CSI. Second, CNN is adopted to solve the problems of computational redundancy and high algorithm complexity which are often occurred by machine learning (ML) algorithms. Third, we introduce an attention mechanism to deal with the weak robustness of CNN models. Finally, simulation results are provided to confirm the proposed method in three aspects, high recognition performance, computational complexity, and robustness. Compared with CNN, LSTM, and other networks, the proposed CNN-ABLSTM method improves the recognition accuracy by up to 4%, and significantly reduces the calculation rate. Moreover, it still retains 97% accuracy under the different scenes, reflecting a certain robustness.

The topic of anonymous unmanned aerial vehicle (UAV) localizing based on angle estimation has been frequently discussed in the past few years. However, the existing methodologies are inefficient in a massive sensor arrays scenario. To avoid such drawback, a cooperative 3-D positioning methodology is introduced. The critical idea of the proposed localizing method is to estimate the 2-D angle of the anonymous UAV via a polarized massive–multi-input multi-output (MIMO) system. To reduce the computational burden and explore the nature of the multidimensional data, a tensor compressive sampling (TCS) framework is proposed. Moreover, a closed-form estimation strategy is developed for 2-D direction finding. Our framework is shown to be more efficient than the existing algorithm in terms of hardware/software complexity. Besides, it is suitable for a polarized MIMO system with an arbitrary array geometry. Several simulation examples are provided to show its improvement of the new methodology.

Malware traffic classification (MTC) is a very important component of cyber security, and a number of the MTC techniques are based on deep learning (DL) with a strong capability of feature mining and classification. However, these DL-based MTC methods are heavily dependent on a large amount of network traffic samples. In the few-shot scenarios, these methods usually overfit and have poor classification performance. Considering that the update cycle of malware is faster and faster, and there are more and more types of malware, collecting enough training samples for all malware is very challenging, if not impossible. In this paper, a novel few-shot MTC(FS-MTC) method is proposed based on convolutional neural network (CNN) and model-agnostic meta-learning (MAML) algorithm. Specifically, the CNN is trained on samples from normal softwares by MAML rather than the conventional optimization methods, then the CNN is finetuned by a few samples from malware for MTC. Simulation results show that our proposed MAML-based FS-MTC can outperform the traditional MTC methods. The performance of our proposed method can reach up to 95.69%.

To address the problem of spectrum resources and transmitting power for vehicular networks, this paper proposes a resource allocation (RA) method based on dueling double deep-Q network (D3QN) reinforcement learning (RL). Due to the high mobility of the vehicle, the channel changes rapidly which makes it difficult to accurately collect high-accuracy channel state information at the base station and to perform centralized management. In response of this difficulty, we construct a multi-intelligence model, using Manhattan Grid Layout City Model as the basis of environment and with each vehicle-to-vehicle (V2V) link as an intelligence. They work together to interact with the environment, receive appropriate observations, get rewards, and finally learn to improve the allocation of power and spectrum to enable users to achieve a better entertainment experience and a safer driving environment. Experimental results demonstrate that with proper training mechanism and reward function construction, cooperation among multiple intelligence can be performed in a distributed manner, with improvements in both the capacity of total vehicle-to-infrastructure links and the effective payload delivery success rate of the V2V links compared to common Q-network.

This paper studies a new framework of aerial reconfigurable intelligent surface (ARIS) assisted non-orthogonal multiple access (NOMA) for wireless communication systems. The base station transmits superimposed signals to multiple users with different channel gains through ARIS which can be deployed flexible. The placement of the unmanned aerial vehicle (UAV) and the passive beamforming of the ARIS are jointly optimized to maximize the sum rate. The non-convex problem is decomposed into two subproblems and solved alternately through the successive convex approximation (SCA). The numerical results show that our proposed NOMA-ARIS framework can achieve better sum rate performance than traditional NOMA with fixed RIS and OMA-ARIS.

The application of deep learning (DL) in the field of network intrusion detection (NID) has yielded remarkable results in recent years. As for malicious traffic classification tasks, numerous DL methods have proved robust and effective with self-designed model architecture. However, the design of model architecture requires substantial professional knowledge and effort of human experts. Neural architecture search (NAS) can automatically search the architecture of the model under the premise of a given optimization goal, which is a subdomain of automatic machine learning (AutoML). After that, Differentiable Architecture Search (DARTS) has been proposed by formulating architecture search in a differentiable manner, which greatly improves the search efficiency. In this paper, we introduce a model which performs DARTS in the field of malicious traffic classification and search for optimal architecture based on network traffic datasets. In addition, we compare the DARTS method with several common models, including convolutional neural network (CNN), full connect neural network (FC), support vector machine (SVM), and multi-layer Perception (MLP). Simulation results show that the proposed method can achieve the optimal classification accuracy at lower parameters without manual architecture engineering.

Specific emitter identification (SEI) extracts the fingerprint characteristics of emitters according to the subtle differences of transmitted signals, to distinguish different emitter individuals and prevent unauthorized network access. Deep learning (DL) based SEI methods have been proposed to achieve a good identification performance in recent years. However, the existing methods need a massive specific emitter dataset to alleviate model overfitting during the training stage. In this paper, we propose data augmentation (DA) aided few-shot learning method and validate the proposed method using automatic dependent surveillance-broadcast (ADS-B) signals. Specifically, according to the characteristics of ADS-B signals, four DA methods, i.e., flip, rotation, shift, and noise are studied for the proposed method. Experimental results are provided to show that the proposed method improves the recognition accuracy and the model robustness.

The ultra-reliable and low-latency communication (URLLC) is one of the critical scenarios in future communications. Energy efficiency (EE), as an important indicator in URLLC, has attracted more and more attention especially in the fields of industrial internet and automation control, etc. At present, power allocation is considered as an effective method to achieve high EE in URLLC. However, since the EE optimization problem in URLLC is usually formulated in the form of fractions with several statistical constraints, it is difficult to obtain the real time analytical solution. Moreover, the traditional expression based on Shannon formula is no longer applicable. In this paper, we formulate the EE problem of URLLC and adopt an unsupervised learning method to parameterize the power allocation function to be optimized through a deep neural network (DNN). The DNN is trained through the primal-dual iterative algorithm offline, and can be deployed online to achieve real time power allocation results. The numerical results show the effectiveness of the proposed method.

Automatic modulation classification (AMC) is an essential and meaningful technology in the development of cognitive radio. It can judge the modulation mode according to the signal acquired by the receiver. In recent years, the deep learning (DL) method has been used to take the place of modulation signal recognition based on decision theory and pattern recognition, which has achieved very effective results. The development of the neural network classification model focuses on architectural engineering. Discovering state-of-the-art neural network architectures requires substantial prior knowledge and effort of human experts. Neural architecture search (NAS) can be viewed as a subdomain of automatic machine learning (AutoML), which uses a neural network to automatically adjust the structures and parameters to obtain a network that researchers need by following search strategies that maximize performance. In this paper, we propose a differentiable architecture search (DARTS) based AMC method. In addition, we also consider six other methods, including convolutional neural network (CNN), simple recurrent unit (SRU), a convolutional-recurrent neural network (CRFN-CSS), Residual Networks (ResNet), Inception Modules (Inception) and MobileNet. Simulation results show that the proposed method can achieve the optimal classification accuracy at low parameters and floating-point operations (FLOPs) without manual architecture engineering.