Two-Stage Distributionally Robust Optimization Framework for Secure Communications in Aerial-RIS Systems
Zhongming Feng, Qiling Gao, Zeping Sui, Yun Lin, Michail Matthaiou
TL;DR
The paper tackles secure mmWave communication with an aerial RIS by addressing multi-timescale uncertainties from user mobility, CSI errors, and hardware imperfections. It introduces a two-stage DRO-CVaR framework that decouples UAV deployment from per-slot beamforming, using a surrogate-assisted CVaR approach for deployment and a Wasserstein-ball SAA-based AO algorithm for robust real-time beamforming. The method yields significant improvements in tail secrecy spectral efficiency and lower outage probabilities compared with benchmark schemes, especially under severe uncertainty. This approach offers a practical, distribution-free robustness mechanism for secure A-RIS systems in dynamic urban environments.
Abstract
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.