Performance Evaluation of Modal Stage SPGD Algorithm for FSOC System

Sensor-less adaptive optics (SLAO) using stochastic parallel gradient descent (SPGD) offers a promising solution for wavefront correction in free-space optical communication (FSOC) systems, as it eliminates the need for conventional wavefront sensors. However, the standard SPGD algorithm’s convergence speed is limited, and it is prone to becoming trapped in local extrema, especially under complex, high-dimensional wavefront distortions in large-scale and dynamic FSOC systems, hindering its use in time-sensitive, high-precision scenarios. To address these limitations, we propose a novel Modal Stage SPGD (MSSPGD) algorithm which integrates subspace optimization techniques with the traditional SPGD algorithm. By projecting the control problem onto a reduced-dimensional Zernike modal subspace and adaptively expanding controlled modes number based on performance metric, our approach decomposes the high-dimensional optimization task into a coarse to fine search optimization problem, thereby accelerating convergence speed, reducing computational complexity, and enhancing robustness against local optima. Theoretical analysis and numerical simulations demonstrate that the proposed algorithm improves convergence speed, stability, and adaptability leading to more effective mitigation of turbulence-induced degradation in critical FSOC metrics. Experimental results further show that the MSSPGD algorithm achieves an approximately 25% reduction in iteration count compared to conventional SPGD. These enhancements prove that the algorithm highly suitable for real-time SLAO in demanding high-speed FSOC systems.