VDNGWO-based aberration correction in sensorless adaptive optics for coherent free-space optical communication

Sensorless adaptive optics (SLAO) is an effective solution for compensating for the effects of atmospheric turbulence in coherent free-space optical communication (CFSOC) systems. We propose a velocity-driven, dimension-learning-based gray wolf optimizer (GWO) with nonlinear population size reduction (VDNGWO), specifically developed to address the limitations of existing SLAO control algorithms for wavefront aberration correction. Theoretical analyses and numerical simulation results show that the VDNGWO algorithm offers faster convergence speed and stronger robustness in coping with complex and variable atmospheric turbulence environments. Compared with the GWO algorithm, the VDNGWO algorithm enhances the speed of correction by approximately 81.5% under strong turbulence and 50.6% under weak turbulence. This algorithm is applied to an SLAO system, which can improve the communication performance of CFSOC systems.