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.

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.

Cognitive radio systems have a recognized need for Zero-Shot Automatic Modulation Recognition (ZSAMR). Previous research aimed at identifying unobserved modulation types struggles to incorporate text-format expert insights into zero-shot learning approaches for unseen modulation schemes. To overcome these limitations, we propose a novel ZSAMR framework utilizing a vision-language model (ZSAMR-VLM). The ZSAMR-VLM leverages the pre-trained vision-language model to describe multiple views of signals with preset prompts. Modulation scheme prediction is achieved by matching multi-view signal characteristics with manually defined semantic prototypes. By facilitating linguistic interactions, the ZSAMR-VLM allows signal processing experts to embed domain-specific knowledge directly into the modulation recognition process, effectively bridging the gap between modulation recognition and natural language. Our simulation experiments demonstrate that the ZSAMR-VLM can effectively implement zero-shot recognition without relying on instances from seen classes. Furthermore, comparative experiments suggest that incorporating multiple views of communication signals significantly enhances the performance of the ZSAMR-VLM. The source code is available at https://github.com/zhaoyurui/ZSAMR-VLM.

Radar jamming waveform design is a vital part of radar jamming. Over seven to eight decades of evolution, the field has transitioned from noise signal design to coherent jamming signal design, resulting in a multitude of complex jamming styles capable of achieving deceptive jamming, suppressive jamming, and smart noise jamming, which combines both deception and suppression. For the first time, this article establishes a general formula for unifying jamming waveform design. Building upon this foundation, we systematically categorize jamming techniques, and provide an in-depth summary of the corresponding principles and conduct comparative analysis of their effectiveness against different targets. Finally, we look forward in terms of future research directions in jamming waveform design, including quantifiable jamming effect evaluation indicators, combined jamming styles, and intelligent jamming waveform generation methods to address the continuous advancement of radar technology and the complexity and variability of the electromagnetic environment. In order to fill the gap in the literature by summarizing the current state of the field and highlighting key challenges and opportunities, this review provides a comprehensive overview of radar jamming waveform design, categorizes and compares different jamming techniques, and identifies future research directions.