Adaptive Suppression Techniques for Atmospheric Turbulence in Space Optical Communications

Abstract

The turbulence mitigation capabilities of space optical communications are undergoing a transformation from classical compensation to intelligent adaptive suppression. This paper systematically reviewing the adaptive suppression techniques for atmospheric turbulence in space optical communications, including theoretical foundations, implementation methods, integration approaches and future trends. It summarizes the contributions of adaptive optics, adaptive modulation, and channel coding, and analyses research progress in intelligent optimization techniques such as genetic algorithms and deep learning for channel estimation and power transmission. It also explores the core bottlenecks of traditional statistical modelling and data-driven methods in terms of system bandwidth limitations, mismatches in physical responses, and engineering generalizability. Finally, addressing current technical challenges such as insufficient detection bandwidth and poor generalization of deep learning models, the technology prospect of physics information neural network, meta-learning and heterogeneous architecture of hybrid space optical communication and radio frequency systems is proposed. By combining low-latency closed-loop control with generative model data augmentation, the aim is to enhance the system's adaptive capabilities and engineering feasibility under dynamic, non-stationary channels. This paper aims to provide a systematic framework for refining the adaptive suppression technology for atmospheric turbulence and promoting the development of space optical communications towards intelligence and high robustness.