Specific emitter identification (SEI) refers to the technology that uses the inherent defects of the physical layer of a hardware device to identify and uniquely associate a single emitter. Given its unique capability to automatically extract high-dimensional features, deep learning has recently shown great potential in SEI. However, the training process of supervised neural networks largely depends on the generalization of sample distribution and integrity of data labels. In specific non-cooperative areas such as electronic reconnaissance and spectrum monitoring, the unknown radio frequency devices and the complexity of the electromagnetic environment make it difficult to utilize sufficient labeled samples and capture the potential distribution of the data. Therefore, we introduce semi-supervised learning into SEI and propose a self-classification generative adversarial network (GAN) using bispectrum-based feature extraction. The bispectrum estimation of the signal is used as the feature representation of emitters, and the label information is embedded into the input latent layer to guide the training of the GAN. The shared weight update of the classification network is realized through semi-supervised training, and the optimization function of the generator is redefined to solve the mode collapse caused by the game principle. We innovatively extend SEI to communication scenarios with multiple relays and evaluate the effectiveness of our algorithm in different communication scenarios with contaminated radio-frequency fingerprints. The algorithm is also verified using Universal Software Radio Peripheral based on software-defined radio platforms. The numerical experimental results on six modulation signals demonstrate the excellent semi-supervised classification performance of the proposed SEI scheme in multiple scenarios.

Specific emitter identification (SEI) uses the unintentional modulation information carried by the emitter waveform, i.e., radio frequency fingerprints, to realize the matching identification of the received signal and its corresponding emitter. We propose an emitter identification scheme based on deep learning (DL). The received signal is subjected to time-varying filtered empirical mode decomposition (tvf-EMD). The obtained intrinsic mode functions (IMFs) are subjected to Hilbert transformation to obtain a 3D-Hilbert spectrum, using amplitude frequency aggregation characteristics obtained by projection to express the nonstationary information of the signal and calculate its bispectral diagonal slice as the second feature. A squeeze-and-excitation block (SEB) is introduced to a convolutional neural network (CNN) to focus on the effective area in the feature map, and support vector machine (SVM) is used to fuse the decision information of the two types of features. We collect four types of steady-state signals on a software-defined radio (SDR) sensor platform based on GNU Radio and universal software radio peripherals (USRP), and study the algorithm performance. The results show that our scheme has strong generalization, high recognition accuracy, and broad application prospects compared with other SEI methods.

Specific emitter identification (SEI) is an emerging device authentication technology, which depends on the inherent hardware characteristics of wireless devices. By analysing the received signal, the hardware characteristics of a specific emitter can be extracted at the receiver and used for device authentication and association. Most of the existing SEI schemes focus on the identification under closed sets. In view of the explosive growth of the number of IoT devices, based on generative adversarial networks, this study proposes a method that considers the identification of unknown emitters. The reconstruction network to reconstruct the signals of the known class and fully train the feature space of the signals of the known class is designed. In the discriminator, two channels are specifically designed to perform anomaly detection for unknown signals and end‐to‐end closed‐set classification for known signals. In order to better reject unknown signals and accept known signals, Receiver operating characteristic curve and Youden index are used to determine the optimal threshold for anomaly detection. Under the given optimal threshold conditions, the identified threshold points have larger True Positive Rate. In addition, the time‐frequency feature combination vector is designed to reveal the essential characteristics of emitters. The experimental results on the real‐world datasets collected from universal software radio peripherals in short‐range communication scenario show that compared with the existing open‐set recognition methods such as C2AE, Openmax and SoftMax, the average recognition accuracy of the proposed framework improved by 0.08, 0.15, and 0.2 respectively, and the Marco‐F1 score improved by 0.05, 0.1, and 0.16, respectively, which proves the superiority of the proposed framework. In addition to identifying unknown and known Universal software radio peripherals in this article, some potential and widespread applications of the proposed framework include access user security authentication in IoT, such as Hack RF and Blue tooth devices. In addition, as a relatively general signal processing framework, the model can be used for air safety management and maritime ship identity authentication in civil aspects. In the military aspect, it can be used in electronic support and various signal reconnaissance scenarios, such as automatic signal modulation recognition in open set scenarios, individual identity determination of radar emitter and unknown working state identification of transmitters, which proves reference for subsequent research on open‐set signal recognition.

The deep learning (DL) enabled semantic communication technology has been revealed its capability in improving the transmission efficiency by recent fruitful research. Speech transmission is a vital application for semantic communication, and the emotion contained in a speech are important in accurately understanding the speaker (i.e., transmitter). This paper proposes an emotional information assisted semantic communication framework for speech transmission. In this framework, the content semantic and the emotional semantic of speech are jointly extracted to achieve semantic consistency between the transmitter and the receiver. Specifically, the emotion-semantic fusion mechanisms are designed to enhance the correlation between the content and the emotion at the receiver. The speech emotion recognition (SER) task is also considered and the intermediate feature of classification is used to construct emotion perception loss, ensuring perceptual similarity between the reconstructed speech and expected emotion category at the utterance level. Extensive simulation results demonstrate that the proposed framework can significantly improve the speech transmission performance.

By focusing on the intrinsic meaning of information, semantic communication (SemCom) marks a fundamental paradigm shift from physical bit transmission to personalized semantic service. Considering the importance of personalized features related to the speaker in speech for source recovery and understanding, we propose a semantic-driven framework for personalized speech transmission, named PerSemCom, which combines speaker acoustic features with semantic information. Specifically, we first introduce an efficient semantic extraction mechanism to achieve the conversion from speech to text transcriptions, and design a semantic corrector coupled with multi-domain knowledge to mitigate the effects of wireless channel distortion. Building upon the reliable transcriptions at receiver, we further establish a speaker embedding vector knowledge base and achieve high-fidelity speech reconstruction through quantitative modeling of speaker-specific acoustic features. Extensive experimental results demonstrate that our proposed framework outperforms existing schemes in terms of subjective perception at harsh channel conditions. Complexity analysis and latency measurements also show competitive advantages in computational efficiency and real-time capabilities. Reconstructed personalized speech samples have been publicly available at https://kwtankw.github.io/PerSemCom/.