Two-Stage Distributionally Robust Optimization Framework for Secure Communications in Aerial-RIS Systems

TL;DR

This paper introduces a two-stage distributionally robust optimization framework for secure aerial-RIS communication systems, effectively handling uncertainties and improving secrecy performance through a novel CVaR-based approach.

Contribution

It develops a novel two-stage DRO framework with CVaR for secure aerial-RIS systems, decoupling UAV placement from beamforming and ensuring robustness under uncertainties.

Findings

Enhanced secrecy spectral efficiency under uncertainty

Lower outage probability compared to benchmarks

Effective robustness against multi-timescale uncertainties

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.