Radio broadcasting plays an important role in our daily life. Meanwhile, with the development of wireless communications, the application of software-defined radio platforms gives rise to cheap and easy design of illegal broadcasting stations. These unauthorized broadcasting stations sometimes illegally occupy licensed frequency band, especially associated with amateur radios and unlicensed personal communication devices and services. These unauthorized broadcasting stations may severely interfere the authorized broadcasting and further disrupt the management of spectrum resource in civil applications, such as emergency services and air traffic control. However, it still remains a challenging task to automatically and effectively identify the unauthorized broadcasting in complicated electromagnetic environments. Aiming at developing an intelligent and efficient unauthorized broadcasting identification system, in this paper, a novel identification approach is proposed based on long short-term memory (LSTM) recurrent neural network (RNN) and LabVIEW software. In our approach, firstly, a series of LabVIEW applications are developed to drive USRP 2930s for the acquisition of broadcasting signals. Then, the LSTM identification network is proposed to recognize unauthorized broadcasting. Through the special gate structure inside, the proposed LSTM framework can effectively extract the distinguishing features, such as channel state information and RF device fingerprinting. Simulation results show that the proposed LSTM based approach perform better than other contrastive methods, especially in identification accuracy. Implementation results also demonstrate that the proposed method has an outstanding unauthorized broadcasting identification performance with a high accuracy, i.e., identify the unauthorized broadcasting signals with 99.83% accuracy at the licensed frequency of 107.8 MHz, in realistic electromagnetic environments.

Automatic modulation classification (AMC) plays an important role in many military and civilian communication applications. However, it remains a challenging task to support such AMC mechanisms under impulsive noise environments. Aiming at improving the classification performance in impulsive noise, in this paper, a novel modulation classification method is proposed by using the cyclic correntropy spectrum (CCES). In the proposed method, CCES is introduced into AMC for effectively suppressing impulsive noise. Specifically, it is verified that modulation types can be distinguished through CCES. Then, Multi-slices are extracted at different cycle-frequencies from CCES as the original features for AMC. Following the extraction, the principal component analysis (PCA) is applied to these slices to further optimize the original features. Finally, the radial basis function (RBF) neural network is used as a classifier to perform modulation classification. Monte Carlo simulations demonstrate that the proposed algorithm outperforms other existing schemes in impulsive noise cases, especially with a low generalized signal to noise ratio (GSNR).

Electromagnetically induced transparency (EIT) originates from quantum physics, where a narrow transparent peak appears in the opaque band due to the destructive interference between quantum states of atoms and molecules. Similar phenomena can be realized based on strong-coupling resonators with a similar spectrum of transmission peaks and abrupt dispersion variations. These classical systems, ranging from elastic to optical, are named analogs of EIT. The sharp resonant peaks with high-quality factors in the spectrum exhibit powerful potentials in sensors with ultra-high sensitivity. In order to better understand the development history of EIT-like metamaterials and their specific applications in the field of sensors, this paper makes a brief review of the EIT-like phenomenon in metamaterials. Firstly, we conduct the universal mathematical formulation based on the coupling oscillator model. Then, we classify specific metamaterial designs and practical applications of EIT-like devices in acoustic, electromagnetic, and optical waves, respectively. We also summarize the recent technologies of dynamic modulations of EIT-like metamaterials and discuss future research directions.

Automatic modulation classification plays an essential role in cognitive communication systems. Traditional automatic modulation classification approaches are primarily developed under Gaussian noise assumptions. Nevertheless, recent empirical studies show that the impulsive noise has emerged in numerous wireless communication systems. The bursty nature of impulsive noise fundamentally challenges the applicability of traditional automatic modulation classification approaches. In order to accurately identify the modulation schemes in impulsive noise environment, in this paper, we propose a novel modulation classification approach through using hyperbolic-tangent cyclic spectrum and multi-branch attention shuffle neural networks. First, based on the designed hyperbolic-tangent autocorrelation function, hyperbolic-tangent cyclic spectrum is proposed to effectively suppress impulsive noise and extract the discriminating features. Then, based on the hyperbolic-tangent cyclic spectrum, a novel deep shuffle neural network is proposed as a classifier to perform the modulation classification through multi-branch attention mechanism to reweight all features. Both numerical and real-data experimental results demonstrate that the proposed algorithm can correctly classify modulation schemes with high accuracy and robustness in impulsive noise.

Automatic Modulation Recognition (AMR) is becoming increasingly important due to its key role in wireless communications. In order to enrich the feature information and reduce computational complexity, a novel feature fusion convolution-aided transformer (FCAformer) approach is proposed in this paper. To enhance the differences between samples, we adopt Markov Transformation Field (MTF), which can extract potential correlation features between signal sequences and combine them with I/Q sequences to generate more information rich data as input for the transformer. We propose a novel patchify module that reduces invalid features and computational complexity by performing feature extraction. We also propose a convolution-aided encoder module consisting of an improved attention mechanism and a convolutional residual connection to improve performance through local-global feature fusion and further reduce the parameters. Experimental results on RadioRML2016.10a show that the proposed AMR method has both higher recognition accuracy and lower parameters than other contrastive methods.

Woven fabric composite structures are applied in a wide range of industrial applications. Composite structures are vulnerable to damage from working in complex conditions and environments, which threatens the safety of the in-service structure. Damage detection based on Lamb waves is one of the most promising structural health monitoring (SHM) techniques for composite materials. In this paper, based on guided Lamb waves, a lightweight deep-learning approach is proposed for identifying damaged regions in woven fabric composite structures. The designed deep neural networks are built using group convolution and depthwise separated convolution, which can reduce the parameters considerably. The input of this model is a multi-channel matrix transformed by a one-dimensional guided wave signal. In addition, channel shuffling is introduced to increase the interaction between features, and a multi-head self-attention module is designed to increase the model’s global modeling capabilities. The relevant experimental results show that the proposed SHM approach can achieve a recognition accuracy of 100% after only eight epochs of training, and the proposed LCANet has only 4.10% of the parameters of contrastive SHM methods, which further validates the effectiveness and reliability of the proposed method.