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Atmospheric Turbulence Identification in a multi-user FSOC using Supervised Machine Learning

Atmospheric turbulence can heavily affect free space optical communication (FSOC) link reliability. This introduces random fluctuations of the received signal intensity, resulting in degraded system communication performance. While extensive research has been conducted to estimate atmospheric turbulence on single user FSOC, the effects of turbulent channel on multi-point FSOC has recently gained attention. In fact, latest results showed the feasibility of multi-user FSOC when users, sharing time and bandwidth resources, communicate with a single optical access node. This paper presents a machine learning (ML)-based methodology to identify how many users are concurrently transmitting and overlapping into a single receiver interfering within each other, and which one is propagating through a turbulent channel. The proposed methodology presents two different approaches based on: 1) traditional classification ML algorithms and 2) Convolutional Neural Network (CNN). Both methods employ amplitude distribution of the received mixed signals as input features. 100% validation accuracy was achieved by CNN employing an experimental data set of 900 images.