This paper investigates a secure aerial reconfigurable intelligent surface (A-RIS) communication system, where user mobility, imperfect channel state information (CSI), and RIS phase errors induced by unmanned aerial vehicle (UAV) jitter significantly degrade performance. To address these challenges, we formulate a joint optimization problem for UAV trajectory, base station (BS) we propose abeamforming, and A-RIS beamforming to maximize the minimum secrecy energy efficiency (SEE), subject to constraints on user secrecy rates and UAV energy efficiency. To solve this highly non-convex problem, we propose a novel reinforcement learning framework termed IA-CSORL based on the twin-twin-delayed deep deterministic policy gradient (TTD3) architecture, which incorporates two novel modules. Specifically, we develop the phase-aware relativistic adaptive descent (PRAD) algorithm is proposed, which embeds the learning process into a conformal Hamiltonian system. By integrating gradient-based phase error correction and adaptive momentum adjustment, PRAD effectively counteracts phase noise and stabilizes training. Furthermore, we design an environment-state interactive attention (ESIA) mechanism to dynamically fuse UAV positioning and environmental features, enhancing state representation and deployment accuracy. Numerical results demonstrate that IA-CSORL significantly outperforms existing RL baselines in terms of both robustness and convergence performance. Moreover, IA-CSORL achieves superior beamforming accuracy under phase errors and CSI imperfections and provides a better trade-off between sum secrecy rate (SSR) and SEE, with performance gains becoming more significant as the number of RIS elements increases.

Low Earth Orbit (LEO) satellite communications have emerged as a promising paradigm for achieving ubiquitous coverage, driving the evolution of space-ground integrated networks (SGINs). The cell-free (CF) architecture has attracted significant attention in SGINs as the terrestrial segment for its potential to enhance capacity and connectivity. However, deploying CF necessitates numerous access points (APs), resulting in a prohibitive cost. To this end, we propose reconfigurable intelligent surface (RIS)- and simultaneous transmitting and reflecting (STAR)-RIS-assisted CF systems for SGINs, where part of the APs is replaced with cost-efficient RISs and STAR-RISs. Non-orthogonal multiple access (NOMA) is incorporated to improve connectivity under limited spectrum. We formulate transmit power minimization problems for both RIS- and STAR-RIS-assisted CF-NOMA in SGINs, jointly optimizing the active beamforming vectors of the satellite and APs, as well as the discrete passive beamforming (DPB) vectors of RISs/STAR-RISs. For the RIS-assisted scenario, a semi-definite programming (SDP)-based method is proposed to optimize the active beamforming vectors, while an enhanced integer linear programming (ILP) method is proposed to obtain the optimal DPB of RISs. To reduce complexity, we develop a low-complexity penalty-based SDP (PB-SDP) algorithm that achieves near-optimal DPB solutions. For the STAR-RIS-assisted scheme, both independent and coupled DPB for transmission and reflection are optimized alone with the active beamforming vectors. Numerical results demonstrate that: 1) The proposed systems outperform cell-based systems and heuristic optimization algorithms in terms of transmit power consumption; 2) The proposed PB-SDP algorithm achieves near-optimal performance with reduced complexity; 3) It is shown that DBP with 3 quantization bits achieves performance comparable to continuous passive beamforming (CPB) in both RIS- and STAR-RIS-assisted systems; 4) Also, it is shown that beyond a certain number of APs, further increasing the APs yields only limited transmit power consumption gains under a fixed total number of antennas.

Specific Emitter Identification (SEI), based on the inevitable hardware imperfections of emitters, plays a crucial role in physical layer security, independent of upper-layer cryptographic methods. In recent years, SEI combined with deep learning has demonstrated exceptional performance and considerable potential. However, most SEI methods are designed for closed, independent identically distributed environments, which contrasts with real-world open scenarios. This discrepancy leads to significant security vulnerabilities when deploying models in practical applications. To address this challenge, we propose a novel Open-Set SEI (OS-SEI) framework, Outlier Exposure with Label Smoothing (OELS), which utilizes partial exposure to auxiliary anomalous data during the training phase and introduces specific loss functions to mitigate the open-set risk. By constructing smooth decision boundaries through label smoothing and applying thresholds to class confidence, our SEI framework demonstrates robust open-set authentication capabilities. Extensive experiments conducted on ADS-B and WiFi datasets show that the OELS method achieves outstanding open-set recognition performance, with F1 scores of 83.77% and 93.14% at an openness level of 0.10557. Furthermore, ablation experiments confirm the effectiveness and potential of our framework’s components. By increasing the diversity of auxiliary unknown data, we further improve performance in OS-SEI tasks.

Specific Emitter Identification (SEI) in wireless communications enhances security by distinguishing devices through their unique RF signal impairments. Deep learning (DL) techniques have become instrumental and have attracted considerable focus in this domain. Nevertheless, significant challenges arise from pronounced domain distribution disparities and the lack of labeled signal for target domain emitters, complicating cross-domain individual emitter identification. This paper proposes an innovative domain generalization framework for SEI, which exploits both intra-domain and inter-domain invariant features to enhance cross-domain robustness. These features significantly enhance the model’s generalization capabilities: intra-domain invariants are captured through the use of Fourier phase information and knowledge distillation, while inter-domain invariants are derived via Maximum Mean Discrepancy (MMD) feature alignment. The proposed methodology exhibits strong performance across three domain generalization scenarios, utilizing datasets SUI-1, SUI-3, and SUI-5. Each dataset comprises signals from seven distinct transmitters, recorded over varying fading channels. This approach achieves an approximate 5% improvement over state-of-the-art SEI domain adaptation methods, highlighting its superior generalization capability. The code and datasets for the paper can be found at: https://github.com/ZHR-HEU/Cross-Domain-Generalization-for-SEI.

Radio Frequency Fingerprinting (RFF) has emerged as a vital technique for enhancing Physical Layer Authentication (PLA) in New Radio (NR) networks. Unlike cryptographic methods, RFF leverages device-specific signal impairments to uniquely identify transmitters. Deep Learning (DL) advances have improved PLA, though challenges persist due to communication channel dynamics and device state changes. In this study, we propose a novel framework that integrates 5G NR protocol-specific structures and channel knowledge via SRS-based CSI to generate relative RFF features. Through a tailored frame design and carefully engineered processing pipeline, we achieve cross-domain stability and improved robustness against time-varying conditions. By applying regularization techniques (e.g., mixup) during training, our method further mitigates model overfitting and domain bias. Simulation and real-world SDR experiments, using data from 9 ADALM-PLUTO devices, validate the approach’s effectiveness. The proposed system attains recognition accuracies of 99.878%, 93.376%, 86.325%, and 66.558% in intra-domain, cross-channel, cross-time, and cross-scenario tests, respectively, highlighting its potential to substantially enhance physical layer security in NR-based networks.

Specific Emitter Identification (SEI) is a critical component of the Industrial Internet of Things (IIoT), enabling effective identification and validation of unauthorized communication devices, thereby preventing malicious interference and signal spoofing. However, SEI methods based on deep learning involve significant computational overhead, and SEI methods based on feature engineering require specialized expertise for feature design, limiting their ability to capture complex patterns. In this paper, we propose a data-knowledge dual-driven adaptive feature fusion approach for specific emitter recognition. Specifically, we present an adaptive feature fusion strategy that integrates domain experts’ prior knowledge with the complex feature recognition capability of deep learning models to achieve efficient SEI classifiers. The approach is evaluated using Automatic Dependent Surveillance-Broadcast (ADS-B) data. The experimental results demonstrate that the proposed method achieves a higher level of identification accuracy and lower time complexity.

In the case of COVID-19, which requires stable and reliable tracking of personnel movement, aircraft identification by specific emitter identification (SEI) is a hot-button issue. It refers to the process of identifying individual aircraft by comparing features extracted from the Radio Frequency (RF) signal of a given aircraft. Deep learning (DL) has been widely used in SEI research due to its excellent feature extraction capability, but in the actual low-resource reception scenario, the aircraft signal data acquired for training are long-tailed in distribution, and the imbalance of the signal data increases the challenge of training the network. In this paper, we propose a novel long-tailed specific emitter identification (LT-SEI) method using decoupled representation (DR) learning. Specifically, we separate the learning process into two stages: representation learning and classification, which includes unbalanced training and balanced classifier learning. The proposed DR-based LT-SEI approach is assessed using aircraft Automatic Dependent Surveillance Broadcast (ADS-B) data collected in the real world and compared to state-of-the-art methods. Experiment results show that the method has better long-tail recognition performance than the existing methods. When the data imbalance factor is 0.01, the F1 score of the model for the recognition result can reach 71.2%, which is 9% higher than that of the baseline model.

To meet the future needs of increasingly crowded airspace, the International Civil Aviation Organization (ICAO ) proposed to use the Automatic Dependent Surveillance-Broadcast (ADS-B ) to provide navigation and surveillance technology to solve the problems of security and capacity in the airspace. But ADS-B does not offer any authentication and encryption. So it is vulnerable to attacks by various illegal devices. A novel radiofrequency fingerprint (RFF ) recognition method of aircraft identity verification based on deep learning is proposed. The ADS-B signal captured by RTL-SDR is used for confirmation. The experimental results show that the fingerprint is called the Contour Stellar Images with a better recognition effect under different networks and different SNR.