Communication device recognition is a key problem of electromagnetic space perception. At present, the traditional recognition technology is difficult to adapt to the complex signal situation. Thanks to the deep learning's superior capability of processing complex and massive data, it has been a hot topic in the field of communication area. However, it needs a large amount and high-quality signal dataset, which will pay for much cost. Therefore, small sample and data augmentation technology should be given much attention. In this article, we propose a novel time series data augmentation technology for small sample recognition. First, a complex neural network is designed to recognize the communication device based on the in-phase/quadrature time series. Second, based on signal data characteristic, several simple and effective methods of time series data augmentation are analyzed, which include noise disturbance, amplitude and time-delay transformation, frequency offset, and phase shift transformation. Third, in order to get a better augmentation result, based on complex neural network model characteristic, a novel data augmentation method of virtual adversarial training is presented for the small sample device recognition. Finally, a series of experimental simulations in real ADS-B signal indicates that the proposed method is suitable for the time series analysis and recognition of communication device with a small sample.

Automatic modulation classification (AMC) is a typical pattern classification task that is an intermediate process between signal detection and demodulation. Deep learning methods used in AMC, such as convolutional neural network (CNN) have their shortcomings. We propose a new parallel CNN transformer network (PCTNet), which not only possesses the advantages of transformer to capture long-range dependencies, but also utilizes the advantages of CNN to extract local information. PCTNet is a parallel design of CNN and transformer, with a delivery mechanism in the middle. Extensive simulation results show that our proposed PCTNet can achieve superior classification performance than traditional deep models.

In recent years, unmanned aerial vehicles (UAV) or drones have become ubiquitous in our daily lives, bringing great convenience to our lives and playing a pivotal role in future wireless networks and the Internet of things. One of the major problems associated with the UAV is the heterogeneous nature of such deployments; this heterogeneity poses many challenges, particularly in the areas of security and privacy. The key to solving these problems is to accurately identify and authenticate drones. In this article, a reliable UAV identify framework based on radio frequency fingerprint is proposed. First, we established a wireless signal label architecture and systematically collected, analyzed, and recorded the radio frequency signals of different UAVs in different flight modes and different distances in the telemetry link, and established UAV signal datasets. Then, the intelligent algorithm and anti-UAV system are designed by using the collected dataset, and the feasibility of the developed dataset for detecting and identifying UAVs is verified by using machine learning and deep learning. The simulation results show that under the condition of Gaussian white noise, the method based on deep learning achieves high reliability, and when the SNR is not less than 5dB, the model achieves more than 95% of the monitoring and recognition accuracy. Finally, we discussed the possible applications of the dataset in the future.

Automatic modulation classification (AMC) is essential in noncooperative communication systems, since it enables the automatic recognition of signal modulation types. The recent incorporation of deep learning, particularly graph neural networks (GNNs), has significantly improved the AMC accuracy. The GNNs increase the performance by decoding the relationships between nodes and edges, which represent the topological structure of data. In AMC, signal features or time points are modeled as nodes, and their interconnections represent the interactions between these features. This modeling allows the GNNs to thoroughly analyze signals and accurately identify complex modulations. However, the existing traditional methods for mapping IQ signal sequences into graphs exhibit high computational load and excessive processing time. To solve these problems, this article proposes a lightweight model of high performance, referred to as PGNet, which combines efficient partial convolution (PConv) with graph sparse attention techniques. This combination minimizes the computational load and maximizes the strengths of the convolutional neural networks and GNNs. The results of the conducted experiment show that PGNet, respectively, achieves average accuracies of 62.8% and 64.1% on the RML2016.10a and RML2016.10b datasets, with only 16315 parameters and an inference time of only 2 ms/sample. Due to its high efficiency and compact size, the proposed PGNet provides a substantial potential for deployment in low computing resource scenarios, such as IoT devices with limited resources.

Automatic Modulation Classification (AMC) is a critical issue in electromagnetic spatial perception. Currently traditional recognition techniques are difficult to adapt to complex signal situations. Most existing modulation classification algorithms ignore the complementarity between different features and the importance of feature fusion. Based on this, we proposed a method for image feature fusion for AMC that fully uses the complementarity between different image features. The original signal is converted into an image by the Gramian Angular Field (GAF) method, and the GAF image is used as the input to the network, meanwhile the received signal is converted from the Inphase-Quadrature (I-Q) domain to the r- $\theta $ domain using the Accumulated Polar Feature conversion technique, and the original signal is feature coded from the r- $\theta $ domain and then converted into an image. The fused features of the two images are used as input to the neural network for model training to achieve automatic modulation classification of multiple types of signals. In the evaluation phase, the differences in the recognition effectiveness of the proposed method by different neural networks are discussed. Experiments show that the best performance is achieved using the Swin-Transformer network model, with a more than 90% recognition rate for the modulation method at signal-to-noise ratios greater than 4dB.

The filtered-x normalized least mean square (FxNLMS) algorithm is widely used in active noise control (ANC) systems. However, for cyclostationary input signals, the noise reduction performance of the FxNLMS algorithm is affected by the periodic power of cyclostationary inputs, which leads to a larger steady-state mean square deviation (MSD) than the filtered-x least mean squares (FxLMS) algorithm. In this paper, a new adaptive combination algorithm of FxNLMS and FxLMS is proposed to suppress cyclostationary input noise with fast convergence and low steady-state MSD. The mixing parameter for combining the two algorithms is obtained by minimizing the energy of the posterior error and updated using the time-average method. The mathematical analysis including the mean square convergence and computational complexity are performed. The simulation results show that the proposed combined-FxNLMS-FxLMS algorithm can efficiently combine the respective advantages of FxNLMS and FxLMS algorithms and improve the noise reduction performance of the ANC system.