Machine Learning Model for Classifying Free Space Optics Channel Impairments

Abstract

Free Space Optics is an optical communication method that uses Free Space instead of Fibre Cable to convey data through a medium from a transmitter to the receiver. It is a viable solution for ensuring high data rates and last-mile communication delivery in Next-Generation wireless communication. However, adverse weather conditions can significantly impair the performance of FSO channel links during transmission. Recently, Machine Learning models have received lots of attention in proffering solutions to signal impairments (that is, atmospheric turbulence, noise, and pointing errors) in optical networks. K-Means clustering algorithm combined with Support Vector Machine (SVM) and K Nearest Neighbour (KNN) classifiers were used for classifying the channel impairments in FSO links in this paper. The Dataset used for the training and testing of the models is fetched from an open-source called “Kaggle”, (https://osapublishing.figshare.com/articles/dataset/Dateset1Freespaceopticalsecretkeyagreement/6850181) cleaned by applying pre-processing techniques, and transformed before being used in the model via MATLAB simulation. The Performance metrics comparison between the two classifiers (K-Means/SVM and K-Means/KNN) suggests that K-means/SVM outperformed K Means/KNN with 99.2% accuracy. The preferred model (K-Means/SVM) is also seen to outperform some existing classification models (K-means with Fuzzy Logic and Random Forest) during the comparison