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Journals / Politeknik Dergisi / 2026 / Cilt: 27 Sayı: 1

Machine Learning Algorithm Estimation and Comparison of Live Network Values of the Inputs Which Have the Most Effect on the FEC Parameter in DWDM Systems

DWDM Sistemlerinde FEC Parametresine En Çok Etki Eden Girdilerin Canlı Ağ Değerlerinin Makine Öğrenimi Algoritması Tahmini ve Karşılaştırılması

Murat Yücel(Gazı University)0000-0002-0349-4013
Mustafa Serdar Osmanca(Türk Telekom)0000-0002-6939-2765
İ. Fatih Mercimek(BOLU ABANT IZZET BAYSAL UNIVERSITY)0000-0002-2179-8803
Pages
133–138
Publication Date
2024-02-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result; In this analysis, which was conducted with 945 live network values from 3 different vendors, it was determined that the most important parameters affecting the FEC value are the number of channels, fiber attenuation, and fiber distance, and these parameters were estimated most accurately with the decision tree machine learning algorithm.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The purpose of this study is to determine the effect of 7 different algorithms on the FEC value, which is one of the most important parameters of the quality measurement metric in DWDM networks, analyzing these changes through machine learning algorithms has determined which parameter is the most important input affecting the FEC parameter according to the live network values.
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Key Findings & Data:To determine the algorithm that gives the most accurate FEC value according to the estimation results in machine learning, it is aimed to make analyzes vendor agnostic.
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Conclusion & Impact:As a result; In this analysis, which was conducted with 945 live network values from 3 different vendors, it was determined that the most important parameters affecting the FEC value are the number of channels, fiber attenuation, and fiber distance, and these parameters were estimated most accurately with the decision tree machine learning algorithm.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:DWDM Performansı · Performans Ölçümü · FEC

Abstract

The purpose of this study is to determine the effect of 7 different algorithms on the FEC value, which is one of the most important parameters of the quality measurement metric in DWDM networks, analyzing these changes through machine learning algorithms has determined which parameter is the most important input affecting the FEC parameter according to the live network values. To determine the algorithm that gives the most accurate FEC value according to the estimation results in machine learning, it is aimed to make analyzes vendor agnostic. As a result; In this analysis, which was conducted with 945 live network values from 3 different vendors, it was determined that the most important parameters affecting the FEC value are the number of channels, fiber attenuation, and fiber distance, and these parameters were estimated most accurately with the decision tree machine learning algorithm.

Özet

Bu çalışmada, telekomünikasyon alanında kullanılan DWDM sistemlerinde kalite metriklerinde en önemli parametrelerden biri olan FEC değerinin, DWDM sistemlerinde toplam 7 farklı metriğe karşı değişimi gözlemlenmiştir. Bu değişiklikler makine öğrenmesi algoritmaları ile analiz edilerek, canlı ağ değerlerine göre FEC parametresini etkileyen en önemli girdinin hangi parametre olduğu belirlenmiştir. Makine öğrenmesinde tahmin sonuçlarına göre en doğru FEC değerini veren algoritmayı belirlemek için vendordan bağımsız analizler yapılması hedeflenmiştir. Sonuç olarak; 3 farklı vendordan 945 canlı ağ değeri ile gerçekleştirilen bu analizde FEC değerini etkileyen en önemli parametrelerin kanal sayısı, fiber zayıflaması, fiber mesafesi olduğu belirlenmiş ve bu parametrenin en doğru şekilde tahmin edilmesi karar ağacı makine öğrenme algoritması ile sağlanmıştır.

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Cite This Article

Yücel, M., Osmanca, M. S., Mercimek, İ. F. (2024). Machine Learning Algorithm Estimation and Comparison of Live Network Values of the Inputs Which Have the Most Effect on the FEC Parameter in DWDM Systems. Politeknik Dergisi, Cilt: 27 Sayı: 1, 133–138. https://doi.org/10.2339/politeknik.1109101

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