Summary of Optimized Quality Of Service Prediction in Fso Links Over South Africa Using Ensemble Learning, by S.o. Adebusola et al.
Optimized Quality of Service prediction in FSO Links over South Africa using Ensemble Learning
by S.O. Adebusola, P.A. Owolawi, J.S. Ojo, P.S. Maswikaneng
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Signal Processing (eess.SP); Optics (physics.optics)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper aims to optimize Quality of Service (QoS) for fibre optic communication systems by developing an ensemble learning model using Random Forest, ADaBoost Regression, Stacking Regression, Gradient Boost Regression, and Multilayer Neural Network. The proposed approach leverages meteorological data from South Africa Weather Services archive (2010-2019) to estimate key parameters such as data rate, power received, fog-induced attenuation, bit error rate, and power penalty. The study demonstrates the effectiveness of ensemble learning techniques in enhancing QoS by optimizing signal-to-noise ratio, meeting customer service level agreements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making communication systems better for fibre optics. Right now, they can be affected by bad weather, which can ruin the quality of service. To solve this problem, the researchers developed a special kind of machine learning called ensemble learning. They used data from South Africa Weather Services to create models that estimate how well the signals will get through different conditions. The results show that this approach can really improve the quality of service and meet customer expectations. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Random forest » Regression
