Bingtao He

This paper investigates a two-stage covert enhancement beamforming design based on the extended Kalman filter (EKF) to combat the mobile eavesdropper (Willie) in the integrated sensing and covert communications. The proposed scheme employs artificial noise (AN) to interfere with and track Willie, while the beamwidth of AN is controlled by the number of antennas. In the initial stage, due to the lack of prior information, the EKF suffers from large prediction errors, preventing the jamming beam from fully aligning with Willie. Thus, a wide AN beam is adopted to guarantee the jamming effect on Willie. In the second stage, as the prediction error of the EKF converges after iterative updates, the scheme switches to a narrow AN beam to track Willie effectively. Considering the imperfect channel state information of the covert user, we jointly optimize the time allocation and transmit beamforming for the two stages, aiming to minimize transmission energy, and propose the joint wide and narrow beam optimization algorithm to address the non-convex problem. Simulation results demonstrate that the proposed scheme reduces the transmission energy by approximately 23% compared with the benchmark schemes.

Emergency scenarios in the Internet of Vehicles (IoV) face significant challenges due to the stringent requirements for ultra-reliable and low-latency communication under high-mobility conditions. This paper proposes a cooperative transmission framework for semantic communication to address these challenges. We introduce a knowledge graph-based approach to represent information as semantic triples (structured entity-relation-attribute representations), whose importance is quantified using a Zipf distribution, enabling prioritized transmission. At the physical layer, a semantic-aware cooperative communication scheme is proposed to combat fading and enhance transmission reliability. The joint optimization of the number of transmitted triples and node power allocation is formulated as a cross-layer problem. To tackle this Mixed-Integer Nonlinear Programming (MINLP) problem with a hybrid action space, we employ the Multi-Pass Deep Q-Network (MP-DQN) algorithm, which is specifically designed for problems with hybrid discrete-continuous action spaces. Simulation results demonstrate that our framework dynamically adapts to channel states and semantic value, achieving up to 85% end-to-end success rate and improving convergence speed by approximately 40% compared to conventional methods.

A covert communication scheme is designed for pinching antenna (PA)-enabled integrated sensing and communication (ISAC) systems. The base station (BS) emits sensing signals to detect the potential eavesdropper while opportunistically performing covert multicast transmissions. To enhance covertness, the inherent power uncertainty of the sensing signals is exploited to confuse eavesdroppers, thereby creating protective coverage for the legitimate transmission. For the considered systems, we design an alternating optimization framework to iteratively optimize the baseband, beamforming, and PA positionson the two waveguides, in which successive convex approximation and particle swarm optimization methods are introduced. Simulated results confirm that the proposed scheme achieves the highest covert communication rates with different numbers of multicast users compared to benchmark methods. Furthermore, increasing the transmit power and the number of PAs can further improve the covertness performance.

An integrated sensing and communication (ISAC) design for pinching antenna systems (PASS) is proposed, where the pinching antennas are deployed to establish reliable line-of-sight communication and sensing links. More particularly, a separated ISAC design is proposed for the two-waveguide PASS, where one waveguide is used to emit the information-bearing signals for ISAC transmission while the other waveguide is used to receive the reflected echo signals. Based on this framework, a penalty-based alternating optimization algorithm is proposed to maximize the illumination power as well as ensure the communication quality-of-service requirement. Numerical results demonstrate that the proposed PASS-ISAC scheme outperforms the conventional antenna scheme.

As a part of Internet of Everything (IoE), satellite communication networks have the problem of the low-emitter identification performance due to limited signal samples. To address this issue, this article develops a novel ground–satellite generative learning (GSGL) framework that deploys the specific emitter identification (SEI) model trained at the ground station to the low-earth orbit (LEO) satellite for identification. In particular, we exploit the embedded temporal information losslessly by employing Gramian angular field (GAF) to preserve the original features of signals, thereby highlighting radio frequency fingerprint (RFF) discriminability among devices. To overcome the limitation of insufficient samples, a hybrid discriminative mechanism driven generative learning is proposed to effectively generate high-fidelity GAF representations, and then the original and generated GAF representations are fed into a residual structure network for extracting RFFs. Considering channel states and generated deviation, we introduce a lightweight combination attention mechanism to reinforce the intraclass cohesion of RFFs by refining the quality of features. Simulation results demonstrate that the proposed framework can enhance interclass separation and intraclass cohesion of RFFs to obtain excellent identification performance under the scenarios of insufficient samples. Furthermore, the proposed framework can achieve 31.56% accuracy improvement over state-of-the-art methods at 5 signal samples per category.

Radio Frequency Fingerprinting Identification (RFFI) is a lightweight physical layer identity authentication technique. It identifies the radio-frequency device by analyzing the signal feature differences caused by the inevitable minor hardware impairments. However, existing RFFI methods based on closed-set recognition struggle to detect unknown unauthorized devices in open environments. Moreover, the feature interference among legitimate devices can further compromise identification accuracy. In this paper, we propose a joint radio frequency fingerprint prediction and siamese comparison (JRFFP-SC) framework for open set recognition. Specifically, we first employ a radio frequency fingerprint prediction network to predict the most probable category result. Then a detailed comparison among the test sample's features with registered samples is performed in a siamese network. The proposed JRFFP-SC framework eliminates inter-class interference and effectively addresses the challenges associated with open set identification. The simulation results show that our proposed JRFFP-SC framework can achieve excellent rogue device detection and generalization capability for classifying devices.

This paper investigates a buffer‐aided cooperative non‐orthogonal multiple access scheme for simultaneous wireless information and power transfer systems, where the near users are equipped with data and energy buffers. To improve the overall energy efficiency, the transmission mode of the considered system adaptively chooses the base station or near‐user transmission mode. Given the target quality of service and limited resources, the long‐term power consumption minimization is formulated by a mixed integer non‐linear programming problem, where the model selection, user scheduling, and power consumption are jointly optimized. To efficiently tackle such a challenging problem, we transform the original problem into an instantaneous non‐convex problem by employing the Lyapunov optimization framework. Then, using the successive convex approximation algorithms, we approximate the instantaneous non‐convex problem as a linear programming problem, where the Karush–Kuhn–Tucker solution of the power allocation can be obtained. The optimality and complexity of the proposed scheme are theoretically analyzed. The simulation results show that the proposed scheme can achieve a near‐optimal performance and significantly improves the energy efficiency compared with the conventional transmission schemes.

This paper designs a helper-assisted offloading strategy in non-orthogonal multiple access enabled mobile edge computing systems, in order to guarantee the quality of service of the energy/delay-sensitive user equipments (UEs). To achieve a tradeoff between the energy consumption and the delay, we introduce a performance metric called energy-delay tradeoff . Aiming at the maximal energy-delay tradeoff minimization, the joint optimization of user association, resource block (RB) assignment, power allocation, task assignment, and computation resource allocation is formulated as a non-convex problem with coupled continuous and 0-1 variables. To tackle this challenging problem, we decompose it as a two-level problem. For the inner-level problem, an iterative parametric convex approximation (IPCA) algorithm is proposed. Then, based on the solution obtained from the inner-level problem, we model the outer-level problem as a four-sided matching problem, and then propose a low-complexity four-sided UE-RB-helper-server matching (FS-URHSM) algorithm. Theoretical analysis demonstrates that the IPCA algorithm can converge to a stationary KKT point and the FS-URHSM algorithm is guaranteed to converge to a stable matching with polynomial complexity. Simulation results demonstrate the superior performance of proposed algorithms in terms of the energy consumption and the delay.