ViT-SPGD: vision transformer-driven stochastic parallel gradient descent for WFS-less adaptive optics in FSO communication

In free-space optical communication (FSOC) using wavefront-sensor-less (WFS-less) adaptive optics (AO), stochastic parallel gradient descent (SPGD) is commonly used to mitigate the adverse effects of atmospheric turbulence (AT) and correct the wavefront distortions. However, conventional SPGD often converges slowly and is prone to becoming trapped in local optima, especially under severe phase aberrations. In this study, we propose ViT-SPGD, a novel wavefront-correction method that integrates a vision transformer (ViT) with SPGD, to significantly improve wavefront correction performance. Specifically, the ViT predicts a deterministic, high-confidence gradient direction while SPGD provides stochastic gradient perturbations. By adaptively fusing the two directions, the hybrid update leverages ViT's learned before accelerate convergence while preserving SPGD's stochastic exploration whenever the ViT guidance is uncertain. We first detail the ViT-SPGD design and fusion mechanism, then validate its performance by comparing it with the original SPGD and two established variants, AdamSPGD and NSPGD. Simulation results, including the system-level dynamic simulations, show that ViT-SPGD substantially accelerates convergence and improves robustness in phase distortion correction. By combining deep learning techniques (i.e., ViT), with traditional iterative optimization (i.e., SPGD), ViT-SPGD provides a practical blueprint for intelligent, high-performance AO and helps pave the way for more robust FSOC operation in atmospheric conditions.