A hybrid algorithmic model for enhancing security in intelligent reflecting surface-assisted wireless communication.

This article introduces Synergistic Gradient Projection with Dynamic Adaptive Risk Expansion (SGP-DARE), a hybrid optimization framework designed to enhance physical-layer security in wireless networks supported by intelligent reflecting surfaces (IRSs). The proposed framework integrates Synergistic Gradient Projection (SGP) for low-complexity joint optimization of base station beamforming and IRS phase shifts, with Dynamic Adaptive Risk Expansion (DARE) ensuring robustness against channel state information (CSI) uncertainties and user mobility. SGP-DARE operates effectively under hardware limitations, including phase quantization, while targeting key objectives such as minimizing secrecy outage probability and improving energy efficiency. Simulation results demonstrate that SGP-DARE significantly outperforms baseline methods in critical metrics of security and efficiency.