针对高空智能表面通信的不确定性，提出两阶段鲁棒优化框架提升安全性能。
Two-Stage Distributionally Robust Optimization Framework for Secure Communications in Aerial-RIS Systems
- 分两阶段优化：先定无人机位置，再实时调整波束方向
- 在严重不确定性下，保密频谱效率提升，中断概率更低
- 适合研究无线安全与智能表面系统的工程师和研究人员
本文提出一种面向高空可重构智能表面（A-RIS）辅助毫米波系统安全部署与波束成形的两阶段分布鲁棒优化（DRO）框架。为应对用户移动、信道状态信息（CSI）不完美及硬件损伤带来的多时变不确定性，方法将长期无人机（UAV）定位与每时隙波束成形设计解耦。采用条件风险价值（CVaR）作为无分布风险度量，设计低复杂度算法：通过代理模型高效完成部署决策，结合交替优化（AO）实现鲁棒实时波束成形。仿真验证表明，所提DRO-CVaR框架在严重不确定性条件下显著提升尾部保密频谱效率，并保持更低的中断概率，优于基准方案。
原文摘要 · Abstract (English)
This letter proposes a two-stage distributionally robust optimization (DRO) framework for secure deployment and beamforming in an aerial reconfigurable intelligent surface (A-RIS) assisted millimeter-wave system. To account for multi-timescale uncertainties arising from user mobility, imperfect channel state information (CSI), and hardware impairments, our approach decouples the long-term unmanned aerial vehicle (UAV) placement from the per-slot beamforming design. By employing the conditional value-at-risk (CVaR) as a distribution-free risk metric, a low-complexity algorithm is developed, which combines a surrogate model for efficient deployment with an alternating optimization (AO) scheme for robust real-time beamforming. Simulation results validate that the proposed DRO-CVaR framework significantly enhances the tail-end secrecy spectral efficiency and maintains a lower outage probability compared to benchmark schemes, especially under severe uncertainty conditions.
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