Analysis of Machine Learning Models for Predicting the Quality Factor and Received Power in Free Space Optical Communication Systems

The study examines the application of machine learning algorithms (MLAs) to enhance free space optical (FSO) communication performance by predicting quality-factor (QF) from key system parameters. FSO technology has developed as a promising solution for front-haul links in 5G, beyond the 5G (B5G), and 6G transmission networks. Nevertheless, performance of an FSO communication link is limited by environmental challenges like weather conditions, attenuation and turbulence that can degrade signal quality and affect QF at the receiving end. Using the data collected through simulation analysis in OptiSystem, we trained several MLAs in order to analyze performance of this system through accurate prediction of the QF. Analysis shows that received power serves as an important parameter in translating the overall QF of the received signal. Furthermore, it is shown that the prediction accuracy of the tree-based approach such as random forest and gradient boosting ranges above 97% are reliant on channel conditions and the predicted parameter type.