Abstract

Advancements in high-speed communication networks, such as 5G and 6G, display the shortcomings of earlier Radio Frequency (RF) systems due to their limited access to the electromagnetic spectrum. Optical Wireless Communication (OWC) gives access to an unlimited optical spectrum that can address the demands in 6G networks. One key thing about Free Space Optical (FSO) is that it uses the near-infrared spectrum to transfer large amounts of data over several kilometers. FSO systems can be found in a large number of places, ranging from home and outdoor use to important roles in the military and in medical settings. These systems, however, struggle to transmit signals clearly and reliably when the distance is very long due to effects of the atmosphere. One solution to these problems is to rely on advanced channel modeling and using Multiple-Input Multiple-Output (MIMO) schemes, as they improve reliability and efficiency. The latest research efforts are centered on Massive MIMO-FSO networks that make use of spatial diversity to fight atmospheric fading and guarantee a sturdier connection. Importantly, Machine Learning (ML) is transforming the way research is carried out. Channel estimation, turbulence prediction, signal demodulation, and adaptive modulation can now be done using ML, which reduces the need for many calculations and makes things run more smoothly. Using information from data, ML helps optimize FSO systems in different channel conditions. This study provides a review of how machine learning is applied in Massive MIMO-FSO systems. It sorts out highlighting current strategies, explaining their strengths, weaknesses, and how to use them. The main goal of this review is to give an in-depth look at how ML-assisted optical wireless systems can fulfill the needs of future communication networks.

Keywords: Radio Frequency; Optical Wireless Communication; Free Space Optical; Massive MIMO; Machine Learning