自由空间光通信中，大气湍流引起的光强闪烁会严重影响通信性能。为了减缓湍流引起的闪烁效应，根据湍流引起光波闪烁的时域特性，给出了一种闪烁动态序列模拟方法，其统计特性可用于自由空间光通信对抗湍流效应的研究。利用一种新的可描述不同湍流强度下光强闪烁的统计模型—双广义伽马分布和大气湍流信道时间协方差函数，应用有色光谱非高斯信号产生算法，建立了可以反映接收光强动态特性的时间序列模型。在考虑光通信系统的瞄准误差时，分析了不同湍流强度下光强闪烁的时间序列模拟，与理论结果吻合较好，且给出了联合概率密度下的平均误码率公式。利用产生的时间序列，对通信系统中的平均误码率和中断概率进行分析。

海洋湍流是制约水下激光通信应用的重要因素之一，相对于大气湍流，海洋湍流在较短距离内即可达到强起伏，针对强湍流传输特性及数值仿真的可靠性验证具有重要意义。基于大气湍流传输特性理论公式，利用大气湍流参数等效表达海洋湍流的方法给出了弱起伏到强起伏条件下海洋湍流传输特性的理论计算公式。针对相位屏法在海洋湍流仿真中的应用，给出了选取相位屏间距、网格尺寸和网格数目的基本要求，并数值仿真了海洋湍流不同参数条件下的传输特性，与理论计算结果进行了对比。结果表明：基于相位屏法得到的光强一阶矩传输特性参量与解析结果较为一致，但是在强闪烁条件下数值仿真光强闪烁特性与理论结果偏差较大。

Training an artificial neural network with backpropagation algorithms to perform advanced machine learning tasks requires an extensive computational process. This paper proposes to implement the backpropagation algorithm optically for in situ training of both linear and nonlinear diffractive optical neural networks, which enables the acceleration of training speed and improvement in energy efficiency on core computing modules. We demonstrate that the gradient of a loss function with respect to the weights of diffractive layers can be accurately calculated by measuring the forward and backward propagated optical fields based on light reciprocity and phase conjunction principles. The diffractive modulation weights are updated by programming a high-speed spatial light modulator to minimize the error between prediction and target output and perform inference tasks at the speed of light. We numerically validate the effectiveness of our approach on simulated networks for various applications. The proposed in situ optical learning architecture achieves accuracy comparable to in silico training with an electronic computer on the tasks of object classification and matrix-vector multiplication, which further allows the diffractive optical neural network to adapt to system imperfections. Also, the self-adaptive property of our approach facilitates the novel application of the network for all-optical imaging through scattering media. The proposed approach paves the way for robust implementation of large-scale diffractive neural networks to perform distinctive tasks all-optically.