Spectrum sensing is one of the technologies that is used to solve the current problem of low utilization of spectrum resources. However, when the signal-to-noise ratio is low, current spectrum sensing methods cannot well-handle a situation in which the prior information of the licensed user signal is lacking. In this paper, a blind spectrum sensing method based on deep learning is proposed that uses three kinds of neural networks together, namely convolutional neural networks, long short-term memory, and fully connected neural networks. Experiments show that the proposed method has better performance than an energy detector, especially when the signal-to-noise ratio is low. At the same time, this paper also analyzes the effect of different long short-term memory layers on detection performance, and explores why the deep-learning-based detector can achieve better performance.

Automatic modulation classification (AMC) is pivotal in both cooperative and non-cooperative communication systems. Despite achieving significant success in this field, deep learning (DL) is challenging to adapt to varying modulation parameters and channel conditions for its reliance on training data. Deep transfer learning (DTL) emerges as a potent tool to address the distribution divergence of the training and testing data, demonstrated by its successful applications in computer vision (CV). Since research on DTL within signal processing remains infantile, this paper offers a comprehensive review of current state-of-the-art (SOTA) research on DTL in modulation classification. The background and theoretical models of DTL are firstly illustrated. Specially, a detailed analysis of how transmission and reception impact data probability density function (PDF) is demonstrated for the first time. Through analyzing current literature, we identify three key classification criteria for DTL-based AMC: 1) what to transfer, 2) relationship between source and target domains or tasks, and 3) availability of labels. In addressing what to transfer, we provide a detailed conclusion and comparison of subclasses, including model-parameter-transfer, feature-representation-transfer, and instance-transfer methods. Lastly, this review discusses recent research trends and outlines future directions for DTL in AMC.

With the advance in wireless network technique, its security becomes of paramount importance. Radio Frequency Fingerprint (RFF) is the underlying characteristic of hardware chains in transmitters, which can be used as a unique ID for specific emitter identification (SEI) and a non-cryptographic access authentication technology in the physical layer to enhance wireless network security. To date, few studies have extracted the inevitable non-linearity in the transmitter as RFF features. Hence, this letter provides a novel nonlinear dynamics approach based on Multi-dimension Approximate Entropy (MApEn) for SEI. Specifically, this method utilizes the steady-state portion of the preamble structure of Standard IEEE802.11b/g to obtain the nonlinear properties of wireless network cards. The experimental results demonstrate that the proposed identification method outperforms the existing steady-state methods in terms of identification accuracy.

Automatic modulation recognition (AMR) is a widely used technique in various communication systems. In this work, we propose a complex-valued transformer (CV-TRN) network for AMR. Considering the in-phase (I) and quadrature (Q) components of the signal are two consistent data with only a phase difference of $\pi $ /2, they can teach the network independently which in disguise augment the training data, but the I/Q components are collectively needed to measure similarity in the multihead self-attention (MHSA). We input the I/Q data individually into the network with shared parameters, and they are transmitted independently in the network except in the MHSA, where a complex-valued MHSA (CMHSA) is proposed to let the information from I/Q components integrate. Moreover, CV-TRN adopts the relative position embedding, with a mathematical analysis of its advantages for AMR. A data augmentation method of random phase offset is introduced to further improve the robustness. Experimental results on RML2016.10a and RML2018.01a data sets demonstrate that the proposed CV-TRN outperforms state-of-the-art AMR methods and is parameter efficient.

The widespread and massive application of multi-function radars (MFRs) pushes MFR modeling to become a recognized need in electronic intelligence (ELINT). Since pioneer research has made trials in the model-centric ELINT from various viewpoints, a universal hierarchical modeling framework is needed at the emergence. To fill the absence and capture future trends, we illustrate the mathematical logic in modeling MFR and propose an MFR modeling framework (named MFRModeling ) by studying and summarizing current literature. To be specific, the MFRModeling contains pulse interception, pulse representation, hierarchical model design, pulse dictation, and evaluation. For the pulse dictation step, we design a novel two-stage processor, comprising of the pulse-to-symbol and symbol-to-symbol transformer. Based on the proposed MFRModeling , current literature’s phases are extracted, grouped, and compared. Finally, recent research trends and the overarching future outlook for MFR modeling in next-generation ELINT are discussed.

To safeguard the privacy and security of IoT systems, specific emitter identification is utilized to recognize device identity with hardware characteristics. In view of the growing demand for identifying unknown devices, this paper aims to discuss open-set specific emitter identification. We firstly build up a problem formulation for open-set SEI by discussing the working mechanisms of radio signals and open-set recognition. And then it is pointed out that feature coincidence is an intractable challenge in open-set SEI. The reason, accounting for this phenome, is that pretrained fingerprint feature extractors are incapable of clustering unknown device features and differentiating them from known ones. Considering that feature coincidence leads to error recognition of unknown devices, we propose to fuse multi-classifiers in the decision layer to improve accuracy and recall. Three distinct inputs and four different fusion methods are adopted in this paper to implement multi-classifier fusion. The datasets collected at Huanghua Airport demonstrate that the proposed method can avoid the coincidence of feature space and achieve higher accuracy and recall.