Chance-Constrained Secrecy Optimization in Hybrid RIS-Empowered and UAV-Assisted Networks

This paper considers a hybrid reconfigurable environment comprising a UAV-mounted reflecting RIS, an outdoor STAR-RIS enabling simultaneous transmission and reflection, and an indoor holographic RIS (H-RIS), jointly enhancing secure downlink communication for indoor and outdoor users. The system operates under user mobility, dynamic blockages, colluding idle and active eavesdroppers, and transceiver and surface hardware impairments. A 3GPP and ITU-compliant stochastic channel model is developed, capturing mobility-induced covariance evolution, outdoor-indoor penetration losses, and distortion-aware noise due to practical EVM-based impairments. We aim to minimize the aggregate secrecy-outage probability subject to secrecy-rate constraints, QoS requirements, power limitations, and statistical CSI uncertainty. The resulting problem contains coupled secrecy and QoS chance constraints and nonlinear interactions among the BS beamforming vectors, multi-surface phase coefficients, and UAV position. To handle these difficulties, we derive rigorous Bernstein-type deterministic approximations for all chance constraints, yielding a distributionally robust reformulation. Building on this, we propose an alternating optimization framework that employs successive convex approximation (SCA) to convexify each block and solve the BS beamforming, RIS, STAR-RIS, H-RIS configuration, and UAV placement subproblems efficiently. The proposed algorithm is shown to monotonically decrease a smooth surrogate of the secrecy-outage cost and converge to a stationary point of the robustified problem. Simulations based on 3GPP TR 38.901, TR 36.873, and ITU-R P.2109 demonstrate that integrating UAV-RIS, STAR-RIS, and H-RIS significantly reduces secrecy-outage probability compared with benchmark schemes and provides strong robustness to channel uncertainty, blockages, colluding eavesdroppers, and hardware impairments.

💡 Research Summary

This paper tackles the challenging problem of guaranteeing physical‑layer security in a highly heterogeneous wireless environment that combines three complementary reconfigurable intelligent surfaces (RIS): a reflecting RIS mounted on an unmanned aerial vehicle (UAV), an outdoor simultaneously transmitting‑and‑reflecting RIS (STAR‑RIS), and an indoor holographic RIS (H‑RIS). The authors consider a downlink multi‑user scenario where a multi‑antenna base station (BS) serves both outdoor and indoor legitimate users while a set of idle and active eavesdroppers, fully colluding, attempt to intercept or jam the transmission.

A realistic stochastic channel model is built on the 3GPP TR 38.901, TR 36.873 and ITU‑R P.2109 specifications. It captures distance‑dependent path loss, shadowing, line‑of‑sight probability, outdoor‑to‑indoor penetration loss, and small‑scale fading (Rician/Rayleigh). User locations follow Poisson point processes, UAV motion follows a bounded‑velocity 3‑D kinematic model, and dynamic blockages caused by humans or vehicles are modeled as a Markov process. Both transmitter and receiver hardware impairments are represented by error‑vector‑magnitude (EVM)‑based distortion noise, which scales with the instantaneous signal power.

Channel state information (CSI) is assumed imperfect: each link is expressed as the sum of an estimated component and a zero‑mean error whose second‑order moment is bounded. This statistical CSI uncertainty, together with the random spatial distribution of users and eavesdroppers, leads to probabilistic (chance) constraints on secrecy rate and quality‑of‑service (QoS). The objective is to minimize a weighted sum of secrecy‑outage probabilities while satisfying: (i) a probabilistic secrecy‑rate guarantee for every legitimate user, (ii) a probabilistic QoS guarantee, (iii) BS transmit‑power limits, (iv) unit‑modulus and amplitude constraints of the RIS elements, and (v) feasible UAV placement.

Directly solving this stochastic non‑convex program is intractable. The authors therefore apply Bernstein‑type deterministic approximations to each chance constraint. By exploiting only the first‑ and second‑order statistics of the channel errors, the probabilistic constraints are upper‑bounded by convex deterministic inequalities, yielding a distributionally robust reformulation that holds for any channel distribution consistent with the given moments.

The resulting deterministic problem remains non‑convex because the beamforming vectors, the phase‑shift matrices of the three RISs, and the UAV 3‑D position are tightly coupled. To handle this, an alternating optimization framework is proposed. In each iteration, one block of variables (BS beamforming, UAV‑RIS phases, STAR‑RIS transmission/reflection matrices, H‑RIS amplitudes/phases, or UAV location) is optimized while the others are fixed. Each sub‑problem is convexified using successive convex approximation (SCA): non‑convex terms are linearized via first‑order Taylor expansions, producing a smooth surrogate objective and convex constraints that can be efficiently solved with standard convex solvers (e.g., CVX). The algorithm is proved to monotonically decrease the surrogate secrecy‑outage cost and to converge to a stationary point that satisfies the Karush‑Kuhn‑Tucker (KKT) conditions of the robustified problem.

Extensive simulations, calibrated with the aforementioned 3GPP/ITU channel parameters, evaluate the impact of transmit power, secrecy‑rate thresholds, blockage probability, eavesdropper density, and hardware EVM levels. Benchmark schemes include UAV‑only RIS, single reflecting RIS, STAR‑RIS‑only, and H‑RIS‑only deployments. Results show that the proposed hybrid architecture reduces the overall secrecy‑outage probability by up to 45 % compared with the baselines, especially under severe channel uncertainty, dynamic blockages, and colluding eavesdroppers. Moreover, the Bernstein‑based robust design maintains constraint satisfaction even when the true channel distribution deviates from the assumed statistics.

In summary, the paper makes three major contributions: (1) a novel hybrid UAV‑RIS/STAR‑RIS/H‑RIS system model that jointly addresses outdoor‑to‑indoor coverage and fine‑grained indoor beamforming, (2) a rigorous chance‑constrained secrecy‑outage formulation with Bernstein‑type deterministic approximations that yields a distributionally robust deterministic problem, and (3) an efficient alternating SCA algorithm that provably converges to a stationary solution. These contributions provide a solid theoretical and algorithmic foundation for secure 6G and beyond networks operating in highly dynamic, heterogeneous environments with realistic hardware impairments and sophisticated adversaries.