Intersatellite optical wireless communication (IsOWC) system has garnered global attention for facilitating high-speed data transfer between two space-based satellites. However, accurately predicting received or output signal power in a lower earth orbit trajectory is challenging due to factors such as background light, scintillation, pointing error, and optical crosstalk. To overcome this problem, a technique based on artificial neural networks (ANN) is proposed to enhance the efficiency of received signal power in the IsOWC system. The input features for an IsOWC system include propagation distance, scintillation attenuation, wavelength, pointing error, and input power, ranging from 1 to 25 km, 0 to 6 dB, 800 to 1600 nm, 0 to 1 µradian, and 0 to 4.77 dBm, respectively. The output feature i.e., received signal power, ranges from − 100 to 34.99 dBm. Before training, exploratory data analysis is performed on 2100 datasets generated by 16-quadrature amplitude modulation based IsOWC system. Furthermore, an ANN model is trained, resulting in a low mean squared error (MSE) of 4.8 × 10^− 6 compared to other machine learning model. The impact of hyperparameter tuning on the MSE curve is rigorously discussed. Additionally, the scatter plot between true power and ANN power prediction, along with an error density plot analysis are thoroughly explored. The proposed technique is intended to efficiently predict the received signal power and find applications in terrestrial communication, military operations, 5G beyond communication, underwater communication, and more for global internet connectivity.

卫星间光无线通信（IsOWC）系统因促进两颗天基卫星之间的高速数据传输而受到全球关注。然而，由于背景光、闪烁、指向误差和光学串扰等因素，准确预测近地轨道轨迹中的接收或输出信号功率具有挑战性。为了克服这个问题，提出了一种基于人工神经网络（ANN）的技术来提高IsOWC系统中接收信号功率的效率。IsOWC 系统的输入特性包括传播距离、闪烁衰减、波长、指向误差和输入功率，范围为 1 至 25 km、0 至 6 dB、800 至 1600 nm、0 至 1 µradian 和 0 至 4.77 dBm ， 分别。输出特征，即接收信号功率，范围为 - 100 至 34.99 dBm。在训练之前，对基于 16 正交幅度调制的 IsOWC 系统生成的 2100 个数据集进行探索性数据分析。此外，ANN 模型经过训练，与其他机器学习模型相比，均方误差 (MSE) 较低，为 4.8 × 10 ^{− 6 。}严格讨论了超参数调整对 MSE 曲线的影响。此外，还深入探讨了真实功率和 ANN 功率预测之间的散点图以及误差密度图分析。所提出的技术旨在有效预测接收信号功率，并在地面通信、军事行动、5G超通信、水下通信等全球互联网连接中找到应用。