Deep Learning Enabled Performance Monitoring of Free Space Optical Communication System

Free space optics is a form of optical communication that uses free space instead of optical fibers to transmit signals. As a result, the optical signal is vulnerable to free-space channel characteristics, such as atmospheric turbulence, fog, rain, smoke, etc., that degrade its quality. Hence, optical performance monitoring seeks to assess the amount of distortion caused by these impairments from the received signals, and predicting the parameters associated with the channel condition aids in the construction of adaptive and reliable optical links. In this paper, we investigate the performance of the FSO communication system under mild to strong turbulent weather conditions. The optical intensity fluctuation at the receiver due to varying channel conditions in the form of scintillation index and the jitter variance, which dictates the turbulence and pointing errors, is first modeled using a statistical approach. Later, we exploit a convolutional neural network (CNN) to predict these parameters. Overall 25 channel scenarios corresponding to various channel conditions are successfully estimated by CNN with a normalized mean square error <-26 dB.