Specific emitter identification (SEI) is a device identification technology that extracts radio frequency (RF) fingerprint from received signals. However, channel effects on RF fingerprint can vary between the training and testing stage, and SEI based on deep learning (DL) will be unable to withstand channel changes. To address this problem, we propose a channel-robust SEI scheme driven by causal learning. We analyze received signals from the causal perspective and construct a structural causal model (SCM) of SEI. In the SCM, received signals are considered as mixtures of the causal element and interference element, and only the former affects identification. Additionally, we design a new RF fingerprint feature representation called the centralized logarithmic power spectrum (CLPS) to reduce the impact of channel effects. Furthermore, we propose a causal purification network (CPNet) driven by causality to further alleviate channel effects. CPNet weakens the spurious associations between the channel and emitter labels through feature decorrelation and feature purification, strengthens the correlation between RF fingerprint and labels, and improves the generalization of SEI. Finally, our approach is evaluated extensively using 20 ZigBee devices under different channel environments. Experimental results demonstrate that our scheme can effectively alleviate channel effects, improve SEI performance under various channel environments, and exhibit good generalization and stability.

Automatic modulation classification (AMC) is gaining greater significance in both military and civilian contexts. However, the diversity and dynamics of actual wireless communication environments can cause shifts in signal data distribution, posing risks of encountering unfamiliar modulations and negatively impacting recognition performance. To tackle these challenges, we propose the Open set domain adaptation for AMC (OSDA-AMC) algorithm. The approach utilizes a partial iterative separation technique, consisting of a pre-processing unit, a deep feature extractor, and meticulously crafted unknown, known, and domain classifiers. OSDA-AMC innovatively introduces an unknown class to the source classifier, facilitating the differentiation between known and unknown class features. Through multiple binary classifiers, the algorithm estimates the similarity between target data and each source class, differentiating unknown and known class data. Iteratively, it separates unknown signals from the target domain and labels them as unknown class. Simultaneously, the known class domain adaptation unit utilizes classifiers and domain discriminator for adversarial domain adaptation within the known class, ensuring similar feature distribution across the two domains. The proposed OSDA-AMC method can enhance the adaptability of AMC for recognizing unknown signals in dynamic channels in real-world environments. The experimental results demonstrate that the algorithm performs better in dynamic communication environments. By utilizing unknown class sample information effectively, we improve the accuracy of recognition and overall robustness.

With the rapid development of wireless communication, automatic modulation recognition (AMR) plays a key role in spectrum management of cognitive radio (CR). However, the dynamic attributes of real-world communication environments, characterized by variations in channels, noise, and other factors, present formidable challenges to AMR systems based on deep learning (DL) technologies. Conventional DL-based AMR approaches, which presuppose data independence and identical distribution (i.i.d.), typically falter in adapting to these perturbations, thereby impeding their efficacy. To rectify this predicament, In this paper, a novel semi-supervised domain-adaptive automatic modulation recognition (SSDA-AMR) method is proposed. The proposed framework seamlessly combines labeled source domain data, sparsely labeled target domain data, and employs semi-supervised domain-adaptive techniques to harmonize features across domains. Data preprocessing encompasses the transformation of in-phase/quadrature (I/Q) signals into enhanced gray-scale contour stellar images (GCSI). By optimizing through the application of adversarial domain-adaptive loss and constraint functions, effective adaptation both inter-domain and intra-domain is achieved. Comprehensive experimentation, conducted on public datasets and custom dataset, conclusively affirms the remarkable generalization capabilities of the SSDA-AMR algorithm for disparate data distributions across various channels.

Specific Emitter Identification (SEI) plays a vital role in the fields of electromagnetic spectrum management and electronic warfare. However, existing SEI algorithms typically require labeled information, which is often unavailable in non-cooperative communication and untrusted scenarios. To tackle these challenges, we propose an algorithm based on multi-scale feature adaptive fusion contrastive learning (MSFAF-CL) for unsupervised recognition. First, a contrastive learning network framework capable of extracting multi-scale features (MSF) of signals is designed. Then, an adaptive feature fusion (AFF) module is introduced to better assign weights to different features, thereby enhancing classification performance. When constructing positive and negative sample pairs for signals, we analyze the limitations of commonly used signal augmentation techniques and propose an algorithm that uses the signal’s In-phase and Quadrature (I/Q) sequence to construct positive and negative sample pairs directly. Extensive experimental results based on the Automatic Dependent Surveillance-Broadcast (ADS-B) individual signal dataset demonstrate that the proposed algorithm can effectively distinguish individual signals under unsupervised conditions, achieving a high recognition rate on a 15-category signal dataset. Furthermore, the algorithm is more efficient compared with the state-of-the-art algorithms.

This article introduces a new concept, MetaRadio, for enabling radios to go beyond traditional radios' physical time and space constraints and form a virtual-real interactive radio system. MetaRadio can connect physical/real space and digital/virtual space to make them grow and evolve together and adapts to dynamic and diversified needs from the whole life cycle of the radio. Specifically, we first present a brief review of radio history, which motivates us to coin the concept of MetaRadio. Then, we describe the unique characteristics of MetaRadio and develop a virtual-real interactive hierarchical framework. Next, we discuss the key enabling technologies. Further, we provide transmitter recognition in an urban environment as a case study to verify the effectiveness of the proposed MetaRadio.