Deep Data Analysis Methods Applied for Hard Switching in Hybrid FSO/RF Systems

The scientific study explores the application of machine learning for predicting the RSSI (Received Signal Strength Indicator) parameter in the context of hybrid communication systems that combine optical and radio transmission technologies. The aim of the study is to analyze the impact of various input data sets on the accuracy of RSSI prediction, utilizing algorithm such as decision trees. The study examines how factors like atmospheric conditions affect RSSI values and the level of accuracy that can be achieved in prediction. The implementation of algorithm is carried out in the PyCharm environment, with a focus on efficient processing of input data. The results suggest that machine learning can significantly contribute to the optimization of hybrid communication systems, thereby improving data transmission quality.