Robust Full-Space Physical Layer Security for STAR-RIS-Aided Wireless Networks: Eavesdropper with Uncertain Location and Channel

TL;DR

This work tackles secure wireless transmission in STAR-RIS aided networks under eavesdropper location uncertainty by deriving an asymptotic average security rate via large-system analysis with only statistical CSI. It then formulates a non-convex optimization problem to maximize a weighted sum of the average security rate and public-user rate, solved through an alternating MMSE-based active beamforming stage and a cross-entropy optimization-based passive beamforming stage. The proposed MMSE-CEO algorithm achieves near-optimal performance with discrete STAR-RIS phase shifts and amplitude constraints, and is shown to mitigate leakage across the full STAR-RIS coverage better than benchmark schemes. The framework offers practical robustness for full-space PLS and provides guidance for implementing STAR-RIS under hardware constraints in future 6G networks.

Abstract

A robust full-space physical layer security (PLS) transmission scheme is proposed in this paper considering the full-space wiretapping challenge of wireless networks supported by simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Different from the existing schemes, the proposed PLS scheme takes account of the uncertainty on the eavesdropper's position within the 360$^\circ$ service area offered by the STAR-RIS. Specifically, the large system analytical method is utilized to derive the asymptotic expression of the average security rate achieved by the security user, considering that the base station (BS) only has the statistical information of the eavesdropper's channel state information (CSI) and the uncertainty of its location. To evaluate the effectiveness of the proposed PLS scheme, we first formulate an optimization problem aimed at maximizing the weighted sum rate of the security user and the public user. This optimization is conducted under the power allocation constraint, and some practical limitations for STAR-RIS implementation, through jointly designing the active and passive beamforming variables. A novel iterative algorithm based on the minimum mean-square error (MMSE) and cross-entropy optimization (CEO) methods is proposed to effectively address the established non-convex optimization problem with discrete variables. Simulation results indicate that the proposed robust PLS scheme can effectively mitigate the information leakage across the entire coverage area of the STAR-RIS-assisted system, leading to superior performance gain when compared to benchmark schemes encompassing traditional RIS-aided scheme.

Figures (8)

• Figure 1: Modeling System for PLS Architecture Assisted by STAR-RIS.
• Figure 2: Iterative curves of the proposed algorithm considering $P_1=P_0=0.5$, $\omega_1=\omega_2=0.5$, $\lambda=2$ and different $M$, $N_\mathrm{t}$ and $P_\mathrm{tmax}$.
• Figure 3: Weighted sum rate versus the number of elements equipped at the STAR-RIS with $P_1=P_0=0.5$, $\omega_1=\omega_2=0.5$, $P_\mathrm{tmax}=30$ dBm, $\lambda=2$ and different $N_\mathrm{t}$.
• Figure 4: Weighted sum rate versus the number of antennas with $P_1=P_0=0.5$, $\omega_1=\omega_2=0.5$, $P_\mathrm{tmax}=30$ dBm, $\lambda=2$ and different $M$.
• Figure 5: Weighted sum rate versus the maximum transmitting power with $P_1=P_0=0.5$, $\omega_1=\omega_2=0.5$, $\lambda=2$, and different $N_\mathrm{t}$ and $M$.

Theorems & Definitions (2)

• proof
• proof