Chen Dan, Wang Rui, Wang Chenhao, Gao Yue, Chen Haoya
Appl Opt. 2024 Mar 20;63(9):2156-2166. doi: 10.1364/AO.514064.
Free space optical (FSO) communication systems experience turbulence-induced fading. As a possible solution, adaptive transmission, which adjusts transmitter parameters based on instantaneous channel state information (CSI), can be used. Most of the existing channel estimation methods ignore the impact of detection noise at the receiver, which will lead to additional estimation errors. In this paper, a joint estimation model based on convolutional neural networks (CNNs) is proposed to estimate detection noise and turbulence fading parameters. We obtained turbulence channel simulation data sets considering the background of detection noise based on the edge probability distribution function of the receive signal. The training of the CNN estimator is carried out through maximum pooling, adaptive learning rate, and regularization, ultimately accurately estimating channel characteristics based on the optimal output results of the network. The simulation results show that the proposed CNN joint estimator performs better in high-detection-noise environments compared with traditional maximum likelihood estimators, and it has better generalization ability in different real atmospheric environments.
自由空间光（FSO）通信系统会经历湍流引起的衰落。作为一种可能的解决方案，可以使用基于瞬时信道状态信息（CSI）调整发射机参数的自适应传输。现有的大多数信道估计方法都忽略了接收机处检测噪声的影响，这会导致额外的估计误差。本文提出了一种基于卷积神经网络（CNN）的联合估计模型，用于估计检测噪声和湍流衰落参数。我们基于接收信号的边缘概率分布函数，获得了考虑检测噪声背景的湍流信道仿真数据集。通过最大池化、自适应学习率和正则化对CNN估计器进行训练，最终根据网络的最优输出结果准确估计信道特性。仿真结果表明，与传统的最大似然估计器相比，所提出的CNN联合估计器在高检测噪声环境中表现更好，并且在不同的实际大气环境中具有更好的泛化能力。
