distributionally robust beamforming

This study addresses the performance degradation of OTFS massive MIMO beamforming caused by channel state information (CSI) uncertainty in high-mobility satellite communications. To tackle this issue, a robust optimization framework based on support vector clustering (SVC) is proposed. By integrating three-dimensional discrete dipole approximation (DDA) channel representation with data-driven SVC to construct asymmetric uncertainty sets, the chance constraints are reformulated as deterministic semidefinite programming problems. Furthermore, a GPU-accelerated alternating direction method of multipliers (ADMM) algorithm is designed for efficient computation. Simulation results demonstrate that the proposed scheme significantly reduces transmit power while enhancing system energy efficiency and robustness. The GPU acceleration achieves substantial computational speedups, thereby validating the overall effectiveness of the proposed framework.

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 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 challenges posed by model mismatch, data scarcity, adversarial perturbations, and distribution shifts in wireless sensing and communication systems by proposing a unified robust signal processing framework. Integrating techniques from robust statistics, distributionally robust optimization, and adversarial training, the framework systematically characterizes the trade-off between performance and robustness. The proposed approach is evaluated across several critical tasks—including robust ranging and localization, multimodal sensing, receive combining, and waveform design—demonstrating significant improvements in system reliability under non-ideal conditions. Experimental results validate the effectiveness and broad applicability of the framework in enhancing resilience against diverse sources of uncertainty inherent in practical wireless environments.

This paper addresses robust multi-user multiple-input multiple-output (MU-MIMO) beamforming under imperfect and statistically unknown channel state information (CSI) errors. Method: We propose a hybrid “offline learning + online adaptation” framework: (i) a shared deep neural network implicitly models the error covariance structure, eliminating reliance on prior statistical assumptions; (ii) a sparse-augmented low-rank (SALR) architecture reduces computational complexity; and (iii) a multi-base model-agnostic meta-learning (MB-MAML) strategy enables dynamic optimal initialization and rapid online gradient fine-tuning. Contribution/Results: The proposed method significantly outperforms state-of-the-art approaches under unknown and non-stationary channel conditions. It achieves strong cross-channel generalization, high robustness against CSI uncertainty, and low online computational overhead—demonstrating superior practicality for real-world deployment.

This study addresses the challenge of guaranteeing user quality-of-service (QoS) in space-air-ground integrated networks (SAGINs), where channel state information (CSI) uncertainty complicates beamforming and resource allocation. To this end, a deep neural network (DNN)-driven robust joint optimization framework is proposed. Specifically, an opportunity-constrained model is first formulated, leveraging a DNN to learn asymmetric CSI uncertainty sets. Subsequently, efficient solutions are achieved by integrating a pre-trained parameter-based robust counterpart approximation with semidefinite relaxation and an adaptive iterative algorithm. Simulation results demonstrate that the proposed approach significantly outperforms conventional schemes in terms of both energy efficiency and robust reliability.

This work addresses the performance degradation and potential data stream outage in millimeter-wave massive MIMO hybrid beamforming caused by channel state information (CSI) errors under conventional quality-of-service (QoS) provisioning schemes. To enhance robustness, the proposed approach leverages channel eigenvectors to guide RF beamforming and introduces a diagonalized baseband precoding structure to mitigate channel uncertainty. By replacing the original uncertainty region with an effective one, the scheme substantially reduces transmit power requirements. Furthermore, it integrates a low-complexity feasibility criterion with a cutting-plane method to iteratively optimize the design, simultaneously eliminating infeasible streams while satisfying per-stream mean-square-error constraints. Simulation results demonstrate that the proposed method significantly lowers transmit power while guaranteeing QoS, exhibiting strong robustness, high feasibility, and low computational complexity.

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.

This work addresses the challenge of ensuring quality-of-service (QoS) in near-field antenna systems when user locations are subject to unknown but bounded errors. To tackle this issue, the study introduces worst-case robust design into such systems for the first time. For single-antenna scenarios, it proposes a convex semidefinite programming (SDP) reformulation based on the S-procedure. In multi-antenna settings, the paper develops a closed-form power allocation scheme coupled with an efficient antenna placement optimization algorithm that integrates block coordinate descent and worst-case channel gain evaluation. The proposed framework rigorously guarantees QoS robustness while achieving power consumption comparable to outage-probability-based benchmark schemes, thereby significantly enhancing both system practicality and energy efficiency.

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.