Enhancing physical layer security through the deployment of Intelligent Reflecting Surfaces (IRS) is an emerging and significant research direction in Six-Generation (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 optimization 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, existing alternating optimization methods face high challenges, particularly for large-scale IRS systems and high-power scenarios. To tackle these issues, we propose a competition mechanism based multi-strategy swarm optimization algorithm, which aims to better balance global exploration and local exploitation capabilities, thereby achieving more optimal IRS configurations. Simulations demonstrate that jointly optimizing IRS amplitude, phase, and AP beamforming significantly improves secrecy rate over phase-only beamforming methods.

When estimating the direction of arrival (DOA) of wideband signals from multiple sources, the performance of sparse Bayesian methods is influenced by the frequency bands occupied by signals in different directions. This is particularly true when multiple signal frequency bands overlap. Message passing algorithms (MPA) with Dirichlet process (DP) prior can be employed in a sparse Bayesian learning (SBL) framework with high precision. However, existing methods suffer from either high complexity or low precision. To address this, we propose a low-complexity DOA estimation algorithm based on a factor graph. This approach introduces two strong constraints via a stretching transformation of the factor graph. The first constraint separates the observation from the DP prior, enabling the application of the unitary approximate message passing (UAMP) algorithm for simplified inference and mitigation of divergence issues. The second constraint compensates for the deviation in estimation angle caused by the grid mismatch problem. Compared to state-of-the-art algorithms, our proposed method offers higher estimation accuracy and lower complexity.