Millimeter-wave rate-splitting multiple access (mmWave-RSMA) technology combines the wideband characteristics of millimeter waves with the rate-splitting mechanism of RSMA, significantly improving the communication efficiency of the system. However, this feature also poses greater challenges to its secure transmission, especially in networks with dual-identity nodes which act as legitimate users while also being able to eavesdrop on information from other users. This behavior will simultaneously weaken the security of both public and private streams. In this regard, the active reconfigurable intelligent surface (RIS) offers a promising method by suppressing eavesdropper channels and enhancing the quality of legitimate links. Focusing on the physical layer security (PLS) challenge posed by dual-identity users in mmWave-RSMA networks, this paper proposes an active RIS assisted secure beamforming scheme integrated with resource allocation optimization. Specifically, while ensuring the quality of service (QoS) for dual-identity user and power amplification constraints of the active RIS, the sum secrecy rate is maximized by jointly optimizing the transmit beamforming vector, reflection coefficient matrix, common rate allocation, and power allocation coefficients. To solve this non convex problem, we divide it into three subproblems. Then, successive convex approximation (SCA) and semidefinite relaxation (SDR) are used to solve these subproblems. Simulation results validated the critical role of active RIS in reducing eavesdropping, and emphasized the importance of joint resource allocation for secure beamforming in mmWave-RSMA.

This paper studies a Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) system operating in the millimeter-wave (mmWave) band to address the energy efficiency demands of urban computation-intensive and delay-sensitive tasks. By exploiting mmWave’s large bandwidth and UAVs’ mobility, we propose a joint optimization framework for UAV deployment, task offloading, and resource allocation to minimize the system energy cost. Since the proposed problem is a mixed-integer nonlinear programming (MINLP) problem with high computational complexity, conventional solvers may fail to find efficient solutions. To address this, we propose an alternating optimization framework. Specifically, the original problem is decomposed into two subproblems: (1) UAV deployment combined with user offloading, and (2) resource allocation, which are solved iteratively. For the former, we employ an obstacle-aware K-means method integrated with coordinate search to optimize UAV positions and user offloading decisions. For the latter, we combine the Lambert W function and the Lagrangian dual method to efficiently allocate communication and computational resources. Simulation results demonstrate that the proposed method substantially lowers the system energy cost compared to baseline methods, presenting a practical solution for urban UAV-MEC systems.

Fluid antennas (FA) offer a revolutionary breakthrough in wireless communications by dynamically regulating antenna positions within confined spaces to significantly enhance system capacity and link reliability. In this paper, a FA integrated and reconfigurable intelligent surface empowered receive spatial modulation (FA-RIS-RSM) scheme is proposed. Specifically, due to the unique physical properties of FA, we propose two port selection strategies, namely the random port selection (RPS) and optimal port selection (OPS), to assist the transmission achieving with distinct extents of reliability. Furthermore, we derive closed-form expressions for the average bit error probability (ABEP), via utilizing the Gamma approximation analysis and correlated fading analogism analysis methods, respectively. Simulated and numerical performance results corroborate the correctness of the derivations, as well as demonstrate the superiority and effectiveness of the proposed FA-RIS-RSM in enhancing the wireless transmission reliability.

Unmanned aerial vehicles (UAVs) have emerged as a promising technology for facilitating communication in the Internet of Things (IoT), owing to their ability to provide enhanced security. However, the communication performance of UAVs can be significantly impacted by the jittering characteristics resulting from random airflow and fuselage vibration. To address this issue, a novel active reconfigurable intelligent surface (RIS) has been developed, which enables a secure and energy-efficient beamforming design. This design takes into account the effect of UAV jittering and involves joint optimization of the reflecting coefficient of the active RIS, beamforming at the UAV-borne base station, and UAV trajectory, subject to worst-case secrecy rate constraints. Unfortunately, the joint optimization problem is nonconvex, making it intractable to solve. To overcome this challenge, the nonconvex problem is reformulated using linear approximation and treated with linear matrix inequality using S-procedure and Schur’s complement. The problem is then decoupled into three subproblems, including UAV trajectory, passive beamforming, and active RIS’s reflecting coefficient optimization, which are solved via alternate optimization. The results of numerical simulations demonstrate that active RIS significantly enhances the power efficiency of secure UAV communication, even under the influence of UAV jitter.

This paper investigates a full-duplex (FD) secure communication system with the assistance of an intelligent reflecting surface (IRS). Compared with the traditional FD system, the IRS-assisted FD communication not only greatly improves the spectrum efficiency but also provides a new way to enhance physical layer security due to the overlapping of multiple signals at the eavesdropper. Furthermore, we consider a more practical scenario without perfect channel state information (CSI) because it is very difficult to obtain the perfect CSI especially for cascaded channels via IRS. In addition, the eavesdropper is usually passive and hidden which will not actively exchange CSI with the user, which leads to an obstacle for obtaining the perfect CSI of eavesdropping channels. To this end, a worst-case achievable security rate (ASR) optimization problem is formulated under the bounded CSI error model. Due to the existence of non-convexity and highly coupled variables, this problem is extremely challenging. To directly tackle the nonconvexity of the considered optimization problem, similar to successive convex approximation (SCA), we first transform the original problem into its equivalent convex optimization problem directly, and finally obtain the optimal solution of the original non-convex problem by iteratively calculating the convex optimization problem. On this basis, we iteratively solve the transmission beamforming and IRS phase shift through Alternate Optimization (AO). In particular, when optimizing the phase shift coefficient, a penalty convex-concave procedure solution is proposed. Simulation results demonstrate that our proposed robust secure beamforming scheme can effectively improve ASR, and also outperforms the nonrobust one.