{"ID":2835321,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.22855","arxiv_id":"2511.22855","title":"Two-Stage Distributionally Robust Optimization Framework for Secure Communications in Aerial-RIS Systems","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.","short_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 informatio...","url_abs":"https://arxiv.org/abs/2511.22855","url_pdf":"https://arxiv.org/pdf/2511.22855v1","authors":"[\"Zhongming Feng\",\"Qiling Gao\",\"Zeping Sui\",\"Yun Lin\",\"Michail Matthaiou\"]","published":"2025-11-28T03:20:19Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.IT\"]","methods":"[]","has_code":false}