Machine learning aided switching scheme for hybrid FSO/RF transmission

Abstract

Free Space Optics (FSO) is one of the technologies which supports immense data transfer requirements. Though it offers high data rate, but experiences atmospheric attenuation due to dynamic weather conditions. On the other hand, RF communication has lower data rates but are comparatively insensitive to weather conditions. This paper focuses on a hybrid FSO/RF system with the application of Machine Learning (ML) on the prediction of Link Margin (LM) and a ML based switching mechanism between FSO/RF based on the current weather conditions. LM is considered as an important quality parameters in the design and analysis of the FSO link. Mainly rain and fog meteorological data are considered for the estimation and classification of link.