Abstract
Free-space optical communications systems offer high bandwidth, increased security and license-free operation but are highly affected by the performance degradation due to the atmospheric attenuation caused by scattering and absorption. The prediction of attenuation accuracy is even more important in Iraq where the environment is hot, dusty, foggy and rainy in a random fashion. The aim of this study is to assess the performance of machine learning, deep learning and hybrid modeling techniques for the prediction of free-space optical communication systems atmospheric attenuation in different Iraqi weather conditions. A synthetic dataset of 1500 samples was created using well established physical propagation models for five weather regimes: clear sky, fog, rain, dust storms and snow. Fifteen predictive models were systematically evaluated, consisting of six machine learning techniques (Random Forest [RF], Extreme Gradient Boosting, Light Gradient Boosting Machine, Support Vector Regression, Linear Regression, and K-Nearest Neighbors), six deep learning architectures (Multilayer Perceptron, Deep Neural Network, Long Short-Term Memory [LSTM], One-Dimensional Convolutional Neural Network [CNN], CNN-LSTM, and Attention-based Network) and three hybrid approaches. The results showed that RF performed the best (R2 = 0.9654, root mean square error = 1.324 dB/km) compared to deep learning approaches (best R2 = 0.7766) and hybrid methods (best R2 = 0.9571). The feature importance was analyzed by Shapley Additive exPlanations and dust concentration (67.3%) and visibility (21.2%) were found to be the most influential factors. While RF resulted in substantially faster training and inference , statistical testing revealed no significant difference between RF and the best performing hybrid approach (p = 0.083). The performance of the conventional machine learning techniques is proved to be highly efficient for the estimation of atmospheric attenuation in FSO communication systems in adverse environmental conditions in agreement with well known physical propagation theories. However, we stress that these conclusions are based on synthetic data, and must be validated by real atmospheric measurements before they can be used in operational FSO systems.