This paper proposes an integrated anti-jamming covert communication and sensing system assisted by reconfigurable intelligent surfaces (RIS). By jointly optimizing beamforming vectors and RIS phase shifts, the system maximizes the sum transmission rate while enhancing communication security, sensing accuracy, and anti-jamming capability. We present two comprehensive optimization schemes: a perfect scheme under ideal channel conditions and a robust scheme for practical scenarios. The perfect scheme jointly optimizes beamforming and phase shifts when perfect channel state information (CSI) is available, establishing a performance upper bound. The robust scheme addresses practical transmission challenges by transforming stochastic uncertainties from imperfect CSI and phase shift errors into deterministic constraints through statistical expectation analysis and worst-case formulations, ensuring reliable system performance under realistic conditions. Both schemes effectively solve the resulting non-convex problems through innovative mathematical reformulations using fractional programming, quadratic transformation techniques, and the alternating direction method of multipliers. Comprehensive simulation results demonstrate significant advantages of our proposed framework in communication reliability, sensing accuracy, and resilience against the jammer compared to conventional approaches.

A robust secure beamforming scheme for the IRS-aided ISAC with imperfect channel state information (CSI) is investigated in this paper, where a device-to-device (D2D) pair is utilized as a cooperative jammer to interfere with the eavesdropping target. Based on a statistical CSI error model, an optimization problem is formulated to minimize the transmit power by jointly optimizing the transmit beamforming and IRS phase shifts, subject to the constraints on the secrecy rate, the D2D communication rate, and the echo signal-to-noise ratio. To address this non-convex problem, we first utilize the Bernstein-type inequality to convert the robust probabilistic constraints into linear matrix inequality forms. Then, it is decomposed into two subproblems, and an alternating optimization algorithm based on the semi-definite relaxation is developed to solve them iteratively. Numerical results verify the effectiveness and robustness of the proposed scheme for secure ISAC.

Although integrated sensing and communication (ISAC) has an advantage of mutual gain of its dual functions, it is susceptible to be eavesdropped by mobile targets due to the broadcast nature of wireless channels. In this paper, we propose a secure predictive beamforming scheme against a mobile eavesdropping target for ISAC, where the intelligent reflecting surface (IRS) is utilized to assist the sensing and secure transmission. To tackle the mobility of eavesdropping target, we first develop a secure predictive beamforming protocol and formulate a sum secrecy rate maximization problem. However, due to the non-convex objective function and the outdated channel state information (CSI), it is difficult to solve the problem directly. Thus, we develop a deep learning based predictive beamforming scheme, which incorporates the parallel convolutional neural network, the long short-term memory modules and the attention mechanism to learn the features from the historical CSI. It can directly design the beamformings for the next time slot with low computational complexity and bypass the need of CSI prediction. Simulation results show that the proposed scheme can significantly enhance the security of ISAC with low overhead.

To achieve sustainable communication and sensing, simultaneous wireless information and power transfer (SWIPT) has been introduced into integrated sensing and communication (ISAC). However, this combination brings significant security challenges due to signal multiplexing and spectrum sharing. In this paper, an active intelligent reflecting surface (IRS) assisted secure integrated sensing and SWIPT system is proposed with the power splitting (PS) model adopted. To maximize the harvested power while satisfying the constraints of sidelobe level ratio and secrecy rate, a problem is formulated to jointly optimize the transmit beamforming, artificial noise (AN) vectors, PS ratios, and amplification factors and phase shifts of active IRS, which is difficult to solve due to the coupled variables. To this end, we decompose it into two sub-problems, and propose two alternating optimization (AO) algorithms to solve them. First, an AO algorithm based on semi-definite relaxation (SDR) is developed. Specifically, we develop a two-layer algorithm to obtain the transmit beamforming matrix, AN covariance matrix and PS ratios, and utilize the penalty-based method to design the coefficients of active IRS. To reduce the complexity caused by the high-dimensional matrix operation of SDR, an AO algorithm based on successive convex approximation (SCA) is proposed, which can approximate the original problem as a sequence of convex counterparts via the first-order Taylor expansion. Simulation results show that the SCA-based AO algorithm can achieve the performance close to that of SDR with lower complexity.

Benefiting from the performance enhancement brought by multistatic cooperative sensing, integrated sensing and communication (ISAC) can capture more environmental information to address the security risk of information leakage caused by the openness of wireless channels. In this paper, we study the multistatic cooperative sensing assisted secure transmission via intelligent reflecting surface (IRS). In particular, we propose a multistatic cooperative sensing scheme to obtain the angle of arrival and the localization of the eavesdropping target to achieve the beam alignment accurately. The goal is to maximize the sum secrecy rate by jointly optimizing the association variables of base station (BS) and users, the BS beamforming and the IRS phase shifts, subject to the requirement of target sensing. Due to the coupling of variables and non-convex objective function, the formulated problem is intractable to solve directly. As such, we decompose it into three subproblems and develop an alternative optimization algorithm to solve them iteratively. The association variables are first optimized by successive convex approximation. Then, the BS transmit beamforming can be derived via the semidefinite relaxation. Finally, we adopt an alternating direction method of multiplier for the IRS phase-shift design. Simulation results indicate the feasibility of the proposed scheme, and the multistatic cooperative sensing via IRS can enhance the sensing performance and guarantee the secure transmission.

Integrated sensing and communication (ISAC), is emerging as a promising technology for future mobile networks. This paper studies the robust secure transmission for intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV)-ISAC networks without eavesdropping channel state information. Particularly, the UAV, as a dual-functional ISAC base station, serves$K$communication users and senses$J$targets with an IRS. Furthermore, an eavesdropper aims at eavesdropping the private information from the UAV to$K$users. Without eavesdropping channel state information, a secure transmission scheme is proposed to maximize the average achievable rate via jointly designing the transmit power allocation, the scheduling of users and targets, the phase shifts at IRS, and the trajectory and velocity of the UAV. Owing to the non-convexity, an iterative algorithm based on the alternating optimization, the successive convex approximation and the manifold optimization is proposed to obtain a sub-optimal solution. Simulation results verify the effectiveness of the proposed scheme.

Most methods for multi-domain adaptive neural machine translation (NMT) currently rely on mixing data from multiple domains in a single model to achieve multi-domain translation. However, this mixing can lead to imbalanced training data, causing the model to focus on training for the large-scale general domain while ignoring the scarce resources of specific domains, resulting in a decrease in translation performance. In this paper, we propose a multi-domain adaptive NMT method based on Domain Data Balancer (DDB) to address the problems of imbalanced data caused by simple fine-tuning. By adding DDB to the Transformer model, we adaptively learn the sampling distribution of each group of training data, replace the maximum likelihood estimation criterion with empirical risk minimization training, and introduce a reward-based iterative update of the bilevel optimizer based on reinforcement learning. Experimental results show that the proposed method improves the baseline model by an average of 1.55 and 0.14 BLEU (Bilingual Evaluation Understudy) scores respectively in English-German and Chinese-English multi-domain NMT.

Integrated sensing and communication (ISAC) systems can suffer from malicious jamming attacks due to the open nature of wireless channels. Deploying aerial intelligent reflecting surface (AIRS) can flexibly configure the propagation environment of ISAC to address this threat. In this paper, we propose an anti-jamming scheme for ISAC via AIRS. Our goal is to maximize the achievable sum rate by jointly optimizing the transmitting beamforming at the dual-function base station, as well as the phase shift matrix and deployment of AIRS, while satisfying the echo signal-to-interference-plus-noise ratio requirement of target sensing. To handle this non-convex problem with multiple coupled variables, we decompose it into three sub-problems and solve them via the alternate optimization. We first introduce auxiliary variables to convert the transmit beamforming sub-problem into a convex counterpart and solve it via semi-definite relaxation. Then, the IRS phase-shift design is transformed into an equivalent rank-constrained problem, and the penalty-based method and the first-order Taylor expansion are leveraged to calculate the passive beamforming. Finally, with the optimized active and passive beamformings, we resort to successive convex approximation to optimize the AIRS deployment. Simulation results are presented to verify the feasibility and effectiveness of the proposed scheme.

Despite its advantage of improving the spectrum and hardware efficiency, integrated sensing and communication (ISAC) system is susceptible to eavesdropping due to the open nature of wireless channels. In this paper, we investigate the secure transmission of ISAC aided by an intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV). Moreover, assuming that an aerial target is a potential eavesdropper, the artificial noise is introduced to disrupt the eavesdropping, while enhancing the sensing signal-to-noise ratio and the users’ quality of service. Aiming to maximize the sum secrecy rate, we jointly optimize the UAV deployment, BS transmit beamforming, artificial noise power and passive beamforming. The formulated non-convex problem is decomposed into three subproblems and solved via an iterative alternating optimization algorithm. Specifically, we introduce auxiliary variables to transform the non-convex subproblems into convex ones. For the UAV deployment solution, it can be obtained by successive convex approximation. With the optimal UAV deployment, the BS transmit beamforming, artificial noise power and passive beamforming can be derived by semi-definite relaxation. Finally, we present simulation results to validate the performance improvement of the proposed scheme on the security of ISAC.

Integrated sensing and communication (ISAC), which can make full use of the wireless platform and the spectrum for concurrent sensing and communication purposes, is emerging as a promising technology for future mobile networks. This paper studies the secure transmission for intelligent reflecting surface (IRS) aided unmanned aerial vehicle (UAV)-ISAC networks. Particularly, the UAV, as a dual-functional ISAC base station, servesKcommunication users and sensesJtargets with the help of an IRS. Furthermore, a potential eavesdropper, whose channel state information is not available, aims at eavesdropping the private information from the UAV toKusers. A secure transmission scheme is proposed to maximize the average achievable rate via jointly designing the transmit power allocation, the scheduling of users and targets, the phase shifts at IRS, as well as the trajectory and velocity of the UAV. Owing to the non-convexity, an iterative algorithm based on the alternating optimization (AO), the successive convex approximation (SCA) and the manifold optimization (MO) is proposed to obtain a near-optimal solution. Moreover, we also investigate the energy efficiency maximization problem. We develop another iterative algorithm based on the AO, the SCA, the MO and the Dinkelbach’s algorithm to obtain a near-optimal solution to this non-convex fractional programming problem. The effectiveness of the proposed schemes is verified via simulation results.

The secure transmission of integrated sensing and communication signals aided by intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) is investigated in this paper, with another aerial target as a potential eavesdropper. Moreover, artificial noise (AN) is introduced to disrupt the eavesdropping, while enhancing the sensing signal-to-noise ratio. To maximize the sum secrecy rate, we jointly optimize the active and passive beamformings, AN power and UAV deployment. The formulated non-convex problem is decomposed into three subproblems and solved with an efficient algorithm iteratively. Specifically, we first introduce auxiliary variables to transform the non-convex subproblems into convex ones. Then, the UAV deployment and active and passive beamformings can be derived by successive convex approximation and semi-definite relaxation, respectively. Finally, we present simulation results to validate the effectiveness of the proposed scheme.

In this paper, an anti-jamming design for integrated sensing and communication (ISAC) via aerial intelligent reflecting surface (AIRS) is proposed. We aim to maximize the achievable sum rate by jointly optimizing the active and passive beamformings, as well as the deployment of AIRS, while satisfying the echo signal-to-interference-plus-noise ratio requirement of target sensing. To address the non-convex problem, we decompose it into three sub-problems and solve them via the alternate optimization. First, the active beamforming is calculated via semi-definite relaxation. Then, the penalty-based method and the first-order Taylor expansion are leveraged to solve the passive beamforming. With the optimized active and passive beamformings, we resort to successive convex approximation to optimize the AIRS deployment. Numerical results validate the effectiveness of the proposed scheme.

Intelligent reflecting surface (IRS) is a promising technique to extend the network coverage and improve spectral efficiency. This paper investigates an IRS-assisted terahertz (THz) multiple-input multiple-output (MIMO)-nonorthogonal multiple access (NOMA) system based on hybrid precoding with the presence of eavesdropper. Two types of sparse RF chain antenna structures are adopted, i.e., sub-connected structure and fully connected structure. First, cluster heads are selected for each beam, and analog precoding based on discrete phase is designed. Then, users are clustered based on channel correlation, and NOMA technology is employed to serve the users. In addition, a low-complexity forced-zero method is utilized to design digital precoding in order to eliminate inter-cluster interference. On this basis, we propose a secure transmission scheme to maximize the sum secrecy rate by jointly optimizing the power allocation and phase shifts of IRS subject to the total transmit power budget, minimal achievable rate requirement of each user, and IRS reflection coefficients. Due to multiple coupled variables, the formulated problem leads to a non-convex issue. We apply the Taylor series expansion and semidefinite programming to convert the original non-convex problem into a convex one. Then, an alternating optimization algorithm is developed to obtain a feasible solution of the original problem. Simulation results verify the convergence of the proposed algorithm, and deploying IRS can bring significant beamforming gains to suppress the eavesdropping.