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

2025-11-27

AI Summary

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.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Optimizes secure deployment and beamforming in aerial-RIS systems
• Addresses multi-timescale uncertainties from mobility and imperfect CSI
• Enhances secrecy spectral efficiency under severe uncertainty conditions

Innovation

• Two-stage DRO framework for aerial RIS systems
• CVaR metric for distribution-free risk management
• Surrogate model with AO for robust beamforming