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.

Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning

“…Compared to [14], this work incorporates considerations for modulation. When determining the link quality model, real-time rain and fog data are used for training, in addition to taking into account the target BER.…”

“…Atmospheric attenuation signifies the reduction in signal power through the atmosphere, rendering it a crucial factor for consideration. Suppose the attenuation coefficient for the FSO link in foggy weather is α 1, f og , which can be derived from Kim's model as [14]…”

“…Ref. [14] proposed a link-switching mechanism based on ML in a hybrid FSO/RF system, the system utilized ML to predict the link margin and achieve link prediction based on weather conditions. As far as we know, the issue of ML-based modulation switching and link threshold for hybrid links considering weather data has not been addressed.…”