Optimized Quality of Service prediction in FSO Links over South Africa using Ensemble Learning

TL;DR

This study employs ensemble learning models to accurately predict and optimize the Quality of Service in free-space optical links in South Africa, accounting for weather impacts over a decade.

Contribution

It introduces the use of ensemble learning techniques for QoS prediction in FSO links, demonstrating high accuracy across multiple locations and weather conditions.

Findings

Ensemble models achieved RMSE below 0.008 across all locations.

High R-squared values indicate excellent model fit.

Optimized QoS enhances service reliability in harsh weather conditions.

Abstract

Fibre optic communication system is expected to increase exponentially in terms of application due to the numerous advantages over copper wires. The optical network evolution presents several advantages such as over long-distance, low-power requirement, higher carrying capacity and high bandwidth among others Such network bandwidth surpasses methods of transmission that include copper cables and microwaves. Despite these benefits, free-space optical communications are severely impacted by harsh weather situations like mist, precipitation, blizzard, fume, soil, and drizzle debris in the atmosphere, all of which have an impact on the Quality of Service (QoS) rendered by the systems. The primary goal of this article is to optimize the QoS using the ensemble learning models Random Forest, ADaBoost Regression, Stacking Regression, Gradient Boost Regression, and Multilayer Neural Network. To accomplish the stated goal, meteorological data, visibility, wind speed, and altitude were obtained from the South Africa Weather Services archive during a ten-year period (2010 to 2019) at four different locations: Polokwane, Kimberley, Bloemfontein, and George. We estimated the data rate, power received, fog-induced attenuation, bit error rate and power penalty using the collected and processed data. The RMSE and R-squared values of the model across all the study locations, Polokwane, Kimberley, Bloemfontein, and George, are 0.0073 and 0.9951, 0.0065 and 0.9998, 0.0060 and 0.9941, and 0.0032 and 0.9906, respectively. The result showed that using ensemble learning techniques in transmission modeling can significantly enhance service quality and meet customer service level agreements and ensemble method was successful in efficiently optimizing the signal to noise ratio, which in turn enhanced the QoS at the point of reception.