Deep Learning-Based Terahertz Phased Array System Beam Compensation Algorithm for Space Radiation Environments

Inter-satellite terahertz (THz) communications, enabled by abundant spectrum and highly directional beams, are a key technology for future space information net-works. However, the extremely narrow beamwidth of THz signals makes the link highly sensitive to beam misalignment. In practical spaceborne THz phased ar-rays, space radiation and inherent hardware imperfections jointly cause radiation pattern distortion and additional pointing errors, leading to severe degradation in link performance. Although traditional optimization methods, such as genetic al-gorithms and convex optimization, have been shown to be effective for beam compensation, they often suffer from local optima, high computational complexi-ty, long convergence time, and limited adaptability. To address these challenges, we propose a deep learning-based beam compensation algorithm for radiation-affected THz phased array systems. We develop a health-aware Res-UNet (HA-Res-UNet) compensation network that leverages array-state feedback for dynam-ic beam correction. We introduce a multi-scale health-mask attention module that embeds element-health priors into feature aggregation to suppress failed elements and emphasize compensable regions. Simulation results demonstrate that the pro-posed method achieves approximately 90.4% pointing error reduction, improves the average pointing accuracy by about 10.4 times, and effectively mitigates radia-tion pattern distortions caused by failed array elements.