@article{Guan2026, 
author = {Shanwen Guan and Xintong Shen and Xinyun Zhang and Ji Li and Li Zhou and Xiaonan Luo},
title = {Joint Beamforming and Intelligent Reflecting Surface Optimization for Enhanced Physical Layer Security},
year = {2026},
journal = {Tsinghua Science and Technology},
keywords = {physical layer security, intelligent reflecting surface (IRS), secrecy rate, competitive swarm optimization},
url = {https://www.sciopen.com/article/10.26599/TST.2026.9010007},
doi = {10.26599/TST.2026.9010007},
abstract = {Enhancing physical layer security through the deployment of intelligent reflecting surfaces (IRS) is an emerging and significant research direction in 6G wireless networks. Existing methods mainly focus on optimizing the transmit beamforming of the access point (AP) and the phase shift of the IRS to maximize the secrecy rate, while neglecting the optimization of the IRS amplitude. We propose a joint op-timization framework that simultaneously optimizes the IRS amplitude, IRS phase shifts, and AP beamforming to enhance the secrecy rate. Due to the problem’s non-convexity, exist-ing alternating optimization methods face high challenges, particularly for large-scale IRS systems and high-power sce-narios. To tackle these issues, we propose a competition mechanism-based multi-strategy swarm optimization algo-rithm, which aims to better balance global exploration and local exploitation capabilities, thereby achieving more opti-mal IRS configurations. Simulations demonstrate that jointly optimizing IRS amplitude, phase, and AP beamforming sig-nificantly improves secrecy rate over phase-only beamforming methods.}
}