beamforming design

This work addresses robust beamforming design for near-field secure integrated sensing and communication (ISAC) systems operating in multi-user, multi-target, and multi-eavesdropper scenarios under channel uncertainty. The problem is formulated as maximizing the sensing beampattern gain subject to minimum SINR constraints for legitimate users, maximum SINR constraints for eavesdroppers, and a total transmit power budget. To tackle the resulting semi-infinite non-convex optimization, we propose a novel integration of the S-procedure and sequential rank-one constraint relaxation (SROCR), transforming the problem into a tractable linear matrix inequality (LMI) form that ensures both rank-one feasibility and low computational complexity. Compared with conventional semidefinite relaxation (SDR), the proposed method achieves superior security and robustness while maintaining high communication quality and sensing performance.

This work addresses the threat posed by adaptive malicious jamming to secure communications under channel state information (CSI) uncertainty by proposing an active reconfigurable intelligent surface (ARIS)-assisted robust anti-jamming scheme. A Stackelberg game is formulated with the legitimate transmitter as the leader and the jammer as the follower, and equilibrium strategies are derived via backward induction. Notably, CSI uncertainty is explicitly incorporated into the ARIS-assisted framework for the first time, and a robust optimization model is developed based on worst-case channel error bounds. The joint design of transmit power allocation, transceiver beamforming, and ARIS reflection coefficients is efficiently solved using the block successive upper-bound minimization (BSUM) algorithm. Simulation results demonstrate that the proposed method significantly outperforms baseline approaches under channel uncertainty, effectively safeguarding legitimate communication performance.

To address the challenge of interference-resilient communication for large-scale aerial reconfigurable intelligent surfaces (ARIS) under adaptive jamming, this paper proposes a continuous robust transmission framework based on mean-field modeling. To overcome the high computational complexity and poor scalability of conventional discrete optimization, we model ARIS deployment as a continuous spatial density function and jointly optimize base station beamforming, ARIS reflection coefficients, and spatial density distribution via variational optimization and Riemannian manifold methods. Theoretically, we characterize a fundamental trade-off between jammer proximity and directionality, and introduce a spatial water-filling principle to guide optimal ARIS resource allocation. Simulation results demonstrate that the proposed framework significantly improves sum rate, achieves computational complexity independent of the number of UAVs, and exhibits strong robustness and scalability—establishing a novel paradigm for large-scale, interference-resilient ARIS deployment.

To address multi-timescale security uncertainties in aerial reconfigurable intelligent surface (A-RIS)-assisted millimeter-wave systems—arising from user mobility, imperfect channel state information (CSI), and hardware impairments—this paper proposes a two-stage distributionally robust optimization framework that decouples long-term UAV deployment from real-time beamforming design. Innovatively, conditional value-at-risk (CVaR) is adopted as a distribution-free risk measure, integrated with surrogate modeling and an alternating optimization algorithm to achieve robust joint optimization under unknown uncertainty sets. Compared with state-of-the-art approaches, the proposed scheme significantly improves tail secrecy spectral efficiency (by 28.6% on average) and reduces the secrecy outage probability (by up to 41.3%), demonstrating superior generalizability and practicality under strong uncertainty conditions.

This work addresses the performance degradation of conventional robust beamforming in near-field communications caused by uncertainty in eavesdropper locations. To tackle this issue, a two-stage robust beamforming approach is proposed that explicitly accounts for the amplification effect of near-field angular errors. By modeling positional uncertainty in the polar coordinate domain and leveraging sector-based subregion partitioning combined with first-order Taylor approximation, the method formulates an optimization framework based on linear matrix inequalities (LMIs). This framework maximizes the sum rate of legitimate users while enforcing a worst-case constraint on the eavesdropping rate. Simulation results demonstrate that the proposed scheme achieves a superior trade-off between secrecy rate and robustness, significantly outperforming existing benchmark methods.

This work addresses the robust beamforming problem for MIMO radar in the presence of bounded perturbations in the prior distribution of target angles. By adopting the worst-case posterior Cramér–Rao bound (PCRB) as the performance metric, the authors formulate an optimization model that minimizes the maximum PCRB. They innovatively model prior uncertainty via a perturbation set and establish, for the first time, a robust beamforming framework based on the worst-case PCRB. Through second-order Taylor expansion and the S-procedure, the originally nonconvex problem with infinitely many constraints is equivalently transformed into a tractable convex optimization problem solvable in polynomial time, yielding a near-globally optimal solution. Numerical experiments demonstrate that the proposed method achieves significantly superior robust sensing performance compared to existing approaches under prior mismatch scenarios.

This work addresses the physical-layer security challenges in satellite communications arising from broadcast transmission, long-distance propagation, and highly dynamic channels. To enhance secrecy performance, the paper proposes a space-based auxiliary reconfigurable intelligent surface (ARIS)-assisted multi-beam secure transmission scheme. By jointly optimizing transmit and reflective beamforming, and modeling channel distribution uncertainty via moment-based ambiguity sets, the authors reformulate probabilistic secrecy constraints into a deterministic form using conditional value-at-risk (CVaR). This yields a distributionally robust secrecy rate maximization model, which is efficiently solved via an alternating optimization algorithm. Numerical results demonstrate that the proposed approach significantly improves system secrecy performance and maintains stable, reliable secrecy rates across various channel error distributions.

Due to great efficiency improvement in resource and hardware space, integrated sensing and communication (ISAC) has gained much attention. In the paper, the physical layer security (PLS) of ISAC system under communication eavesdropper together with sensing eavesdropper is investigated. The system secrecy rate is maximized by transmit beamforming design of communication and sensing signals when taking sensing security, sensing performance and transmit power constraint into consideration. To deal with the formulated non-convex optimization problem, the successive convex approximation (SCA) together with the first-order Taylor expansion and semidefinite relaxation (SDR) is utilized. Additionally, it is theoretically validated that the SDR does not yield sub-optimality in the paper. Thereafter, an iterated joint secure beamforming algorithm against communication and sensing eavesdroppers is proposed. Simulation results validate the effectiveness and advance of the proposed scheme.

This work addresses the joint optimization of communication and sensing in a multi-user integrated sensing and communication (ISAC) system, where both target angles and reflection coefficients are unknown and lack prior information. The authors investigate a downlink multi-antenna base station that simultaneously serves multiple users and performs angle sensing. They propose a unified transmission architecture combining communication beams with dedicated sensing beams and, for the first time, optimize beamforming based on the periodic posterior Cramér–Rao bound (PCRB) without any prior knowledge of reflection coefficients. It is theoretically shown that achieving optimal performance requires at most one dedicated sensing beam. The non-convex problem is solved via semidefinite relaxation and Lagrangian duality to minimize the PCRB under user communication rate constraints. Simulations confirm that the proposed scheme significantly enhances sensing accuracy while maintaining communication quality, validating the theoretical analysis and algorithmic efficacy.

This work addresses beamforming design in MIMO integrated sensing and communication (ISAC) systems under uncertainty, where both target and user locations are unknown but characterized by known probability distributions. The authors formulate an optimization framework that jointly considers statistical sensing and communication performance by minimizing the posterior Cramér–Rao bound (PCRB) subject to a constraint on the expected communication rate. Leveraging location distribution information, they derive a theoretical upper bound on the rank of the optimal transmit covariance matrix and prove that static beamforming suffices to achieve optimality, obviating the need for time-varying designs. Furthermore, they show that ISAC performance improves as the spatial distributions of the target and user become more similar. Numerical simulations corroborate the theoretical findings and offer practical guidance for base station deployment and user–target association.