Research Article
BibTex RIS Cite

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

Year 2024, , 133 - 138, 29.02.2024
https://doi.org/10.2339/politeknik.1109101

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.

References

  • [1] Wen, B., Bhide, N. M., Shenai, R. K., Sivalingam, K. M. “Optical wavelength division multiplexing (WDM) network simulator (OWns): architecture and performance studies”, SPIE Optical Networks Magazine, 2: 16-26, (2001).
  • [2] Bhide, N., Sivalingam, K. M., “Design of a WDM Network Simulator for Routing Algorithm Analysis,” Proc of First Optical Networking Workshop, Dallas, TX, (2000).
  • [3] Mukherjee, B., “WDM Optical Communication Networks: Progress and Challenges”, IEEE Journal on Selected Areas in Communications, 18:1810-1824, (2000).
  • [4] Ramaswami R., Sivarajan, K. N., “Routing and Wavelength Assignment in All-Optical Networks”, IEEE/ACM Trans. Networking, 3: 489-500, (1995).
  • [5] Ramamurthy B., Mukherjee, B., “Wavelength Conversion in WDM Networking”, IEEE Journal on Selected Areas in Communications, 16:1061-1073, (1998).
  • [6] Mitchell, M., “An introduction to genetic algorithms”. MIT press, (1998).
  • [7] Fehenberger, T., Böcherer, G., Alvarado A., Hanik, N., “LDPC coded modulation with probabilistic shaping for optical fiber systems”, Proc. Opt. Fiber Commun. Conf. Exhib, (2015).
  • [8] Zhu K., Mukherjee, B., “Traffic grooming in an optical WDM mesh network”, IEEE JASC, (2002).
  • [9] Essiambre, R. J., Kramer, G., Winzer, P. J., Foschini, G. J., & Goebel, B. “Capacity limits of optical fiber networks”, Journal of Lightwave Technology, 28:662-701,(2010)
  • [10] Buchali, F., Steiner, F., Böcherer, G., Schmalen, L., Schulte P., Idler, W., “Rate adaptation and reach increase by probabilistically shaped 64-QAM: An experimental demonstration”, Journal of Lightwave Technology, 34: 1599-1609, (2016).
  • [11] Liu, J., Wang, G., Hu, P., Duan, L. Y., & Kot, A. C. “Global context-aware attention LSTM networks for 3D action recognition”, In Proceedings of the IEEE conference on computer vision and pattern recognition, 3671–3680 , (2017).
  • [12] Yücel M., Ozturk N. F., “Determination of central wavelength in FBG sensing systems by Gaussian fitting technique”, Journal of Polytechnic, 24(1):63-68, (2021).
  • [13] Saraç, Z. “Hilbert transform approach to central wavelength detection for fiber Bragg grating sensors”, Journal of Polytechnic, 1-1, (2021).
  • [14] Kipriksiz S. E., Yücel M., “Design and implementation of temperature sensor using non-uniform fiber Bragg grating”, Journal of Polytechnic, 24(3): 843-851, (2021).
  • [15] Zibar, D., Piels, M., Jones, R., Schäeffer, C. G. “Machine learning techniques in optical communication”, Journal of Lightwave Technology, 34:1442-1452, (2015).
  • [16] Ye, H., Li, G. Y., Juang, B. H. “Power of deep learning for channel estimation and signal detection in OFDM systems”, IEEE Wireless Communication Letter, 114–117. (2018).
  • [17] Kozdrowski, S., Cichosz, P., Paziewski, P., & Sujecki, S. “Machine learning algorithms for prediction of the quality of transmission in optical networks”, Entropy, (2020).
  • [18] Srisuwarat, W., Akaranuchat, J., & Worasucheep, D. R.” Performance of 10 Gb/s optical receiver in 50-GHz DWDM transmission over 40-km SSMF”, ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 992-995, (2010).
  • [19] Singh, K. P., Singh, N., Dhaliwal, G. S., “Performance Analysis of Different WDM Systems”, International Journal of Engineering Science and Technology (IJTEST), 1140-1144, (2012).
  • [20] Gariépy, D., and Gang He. “Measuring OSNR in WDM systems-Effects of resolution bandwidth and optical rejection ratio”, White paper EXFO Inc, (2009).
  • [21] Rizzi, M. “Automation of optical provisioning on multi-vendor metro optical platforms” Optical Fiber Communication Conference. Optical Society of America, (2017).
  • [22] Pointurier, Y. “Design of low-margin optical networks”, Journal of Optical Communications and Networking, (2017).
  • [23] Natalino, C., Schiano, M., Di Giglio, A., Wosinska, L., & Furdek, M. “Experimental study of machine-learning-based detection and identification of physical-layer attacks in optical networks”, Journal of Lightwave Technology, 37:4173-4182. (2019).
  • [24] Morais, R.M., Pedro, J., “Machine learning models for estimating the quality of transmission in DWDM networks”, IEEE/OSA J. Opt. Commun. Network. (2018).
  • [25] Donner, R., Reiter, M., Langs, G., Peloschek, P., Bischof, H., “Fast active appearance model search using canonical correlation analysis”, IEEE transactions on pattern analysis and machine intelligence, 28:1690-1694, (2006).
  • [26] Clarke, A., Johnston, N.M. “Scaling of metabolic rate with body mass and temperature in teleost fish”, Journal of Animal Ecology, 68:893– 905. (1999).
  • [27] Naghibi, S.A., Pourghasemi, H.R. Dixon, B., “GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran”, Environmental Monitoring And Assessment, 188:1-27 (2016).
  • [28] Su, X., Yan, X., & Tsai, C. L. “Linear regression”, Wiley Interdisciplinary Reviews: Computational Statistics, 4:275-294, (2012).
  • [29] Siqueira, M., Oliveira, J., Curiel, G., Hirata, A., V. Hooft, F., Nascimento, M., Rothenberg, C. E. “An optical SDN controller for transport network virtualization and autonomic operation”, In 2013 IEEE Globecom Workshops (GC Wkshps) , 1198-1203 ,(2013).
  • [30] Li, F., Yang, Y., & Xing, E. “From lasso regression to feature vector machine”, Advances in neural information processing systems, (2005)
  • [31] Kim, E., Lee, M., & Oh, S., “Elastic-net regularization of singular values for robust subspace learning.” In Proceedings of the IEEE conference on computer vision and pattern recognition. 915-923, (2015).
  • [32] Arikawa, M., Nakamura, K., Hosokawa, K., Hayashi, K., “Long-Haul WDM/SDM transmission over coupled 4-core fiber with coupled 4-core EDFA and its mode dependent loss characteristics estimation”, Journal of Lightwave Technology, 40(6):1664-1671, (2022).
  • [33] Yeh, C. H., Liu, L. H., Lin, W. P., Ko, H. S., Lai, Y. T., Chow, C. W., “A survivable optical network for WDM access against fiber breakpoint”, IEEE Access, 10:25828-25833, (2022).

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

Year 2024, , 133 - 138, 29.02.2024
https://doi.org/10.2339/politeknik.1109101

Abstract

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.  

References

  • [1] Wen, B., Bhide, N. M., Shenai, R. K., Sivalingam, K. M. “Optical wavelength division multiplexing (WDM) network simulator (OWns): architecture and performance studies”, SPIE Optical Networks Magazine, 2: 16-26, (2001).
  • [2] Bhide, N., Sivalingam, K. M., “Design of a WDM Network Simulator for Routing Algorithm Analysis,” Proc of First Optical Networking Workshop, Dallas, TX, (2000).
  • [3] Mukherjee, B., “WDM Optical Communication Networks: Progress and Challenges”, IEEE Journal on Selected Areas in Communications, 18:1810-1824, (2000).
  • [4] Ramaswami R., Sivarajan, K. N., “Routing and Wavelength Assignment in All-Optical Networks”, IEEE/ACM Trans. Networking, 3: 489-500, (1995).
  • [5] Ramamurthy B., Mukherjee, B., “Wavelength Conversion in WDM Networking”, IEEE Journal on Selected Areas in Communications, 16:1061-1073, (1998).
  • [6] Mitchell, M., “An introduction to genetic algorithms”. MIT press, (1998).
  • [7] Fehenberger, T., Böcherer, G., Alvarado A., Hanik, N., “LDPC coded modulation with probabilistic shaping for optical fiber systems”, Proc. Opt. Fiber Commun. Conf. Exhib, (2015).
  • [8] Zhu K., Mukherjee, B., “Traffic grooming in an optical WDM mesh network”, IEEE JASC, (2002).
  • [9] Essiambre, R. J., Kramer, G., Winzer, P. J., Foschini, G. J., & Goebel, B. “Capacity limits of optical fiber networks”, Journal of Lightwave Technology, 28:662-701,(2010)
  • [10] Buchali, F., Steiner, F., Böcherer, G., Schmalen, L., Schulte P., Idler, W., “Rate adaptation and reach increase by probabilistically shaped 64-QAM: An experimental demonstration”, Journal of Lightwave Technology, 34: 1599-1609, (2016).
  • [11] Liu, J., Wang, G., Hu, P., Duan, L. Y., & Kot, A. C. “Global context-aware attention LSTM networks for 3D action recognition”, In Proceedings of the IEEE conference on computer vision and pattern recognition, 3671–3680 , (2017).
  • [12] Yücel M., Ozturk N. F., “Determination of central wavelength in FBG sensing systems by Gaussian fitting technique”, Journal of Polytechnic, 24(1):63-68, (2021).
  • [13] Saraç, Z. “Hilbert transform approach to central wavelength detection for fiber Bragg grating sensors”, Journal of Polytechnic, 1-1, (2021).
  • [14] Kipriksiz S. E., Yücel M., “Design and implementation of temperature sensor using non-uniform fiber Bragg grating”, Journal of Polytechnic, 24(3): 843-851, (2021).
  • [15] Zibar, D., Piels, M., Jones, R., Schäeffer, C. G. “Machine learning techniques in optical communication”, Journal of Lightwave Technology, 34:1442-1452, (2015).
  • [16] Ye, H., Li, G. Y., Juang, B. H. “Power of deep learning for channel estimation and signal detection in OFDM systems”, IEEE Wireless Communication Letter, 114–117. (2018).
  • [17] Kozdrowski, S., Cichosz, P., Paziewski, P., & Sujecki, S. “Machine learning algorithms for prediction of the quality of transmission in optical networks”, Entropy, (2020).
  • [18] Srisuwarat, W., Akaranuchat, J., & Worasucheep, D. R.” Performance of 10 Gb/s optical receiver in 50-GHz DWDM transmission over 40-km SSMF”, ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 992-995, (2010).
  • [19] Singh, K. P., Singh, N., Dhaliwal, G. S., “Performance Analysis of Different WDM Systems”, International Journal of Engineering Science and Technology (IJTEST), 1140-1144, (2012).
  • [20] Gariépy, D., and Gang He. “Measuring OSNR in WDM systems-Effects of resolution bandwidth and optical rejection ratio”, White paper EXFO Inc, (2009).
  • [21] Rizzi, M. “Automation of optical provisioning on multi-vendor metro optical platforms” Optical Fiber Communication Conference. Optical Society of America, (2017).
  • [22] Pointurier, Y. “Design of low-margin optical networks”, Journal of Optical Communications and Networking, (2017).
  • [23] Natalino, C., Schiano, M., Di Giglio, A., Wosinska, L., & Furdek, M. “Experimental study of machine-learning-based detection and identification of physical-layer attacks in optical networks”, Journal of Lightwave Technology, 37:4173-4182. (2019).
  • [24] Morais, R.M., Pedro, J., “Machine learning models for estimating the quality of transmission in DWDM networks”, IEEE/OSA J. Opt. Commun. Network. (2018).
  • [25] Donner, R., Reiter, M., Langs, G., Peloschek, P., Bischof, H., “Fast active appearance model search using canonical correlation analysis”, IEEE transactions on pattern analysis and machine intelligence, 28:1690-1694, (2006).
  • [26] Clarke, A., Johnston, N.M. “Scaling of metabolic rate with body mass and temperature in teleost fish”, Journal of Animal Ecology, 68:893– 905. (1999).
  • [27] Naghibi, S.A., Pourghasemi, H.R. Dixon, B., “GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran”, Environmental Monitoring And Assessment, 188:1-27 (2016).
  • [28] Su, X., Yan, X., & Tsai, C. L. “Linear regression”, Wiley Interdisciplinary Reviews: Computational Statistics, 4:275-294, (2012).
  • [29] Siqueira, M., Oliveira, J., Curiel, G., Hirata, A., V. Hooft, F., Nascimento, M., Rothenberg, C. E. “An optical SDN controller for transport network virtualization and autonomic operation”, In 2013 IEEE Globecom Workshops (GC Wkshps) , 1198-1203 ,(2013).
  • [30] Li, F., Yang, Y., & Xing, E. “From lasso regression to feature vector machine”, Advances in neural information processing systems, (2005)
  • [31] Kim, E., Lee, M., & Oh, S., “Elastic-net regularization of singular values for robust subspace learning.” In Proceedings of the IEEE conference on computer vision and pattern recognition. 915-923, (2015).
  • [32] Arikawa, M., Nakamura, K., Hosokawa, K., Hayashi, K., “Long-Haul WDM/SDM transmission over coupled 4-core fiber with coupled 4-core EDFA and its mode dependent loss characteristics estimation”, Journal of Lightwave Technology, 40(6):1664-1671, (2022).
  • [33] Yeh, C. H., Liu, L. H., Lin, W. P., Ko, H. S., Lai, Y. T., Chow, C. W., “A survivable optical network for WDM access against fiber breakpoint”, IEEE Access, 10:25828-25833, (2022).
There are 33 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Murat Yücel 0000-0002-0349-4013

Mustafa Serdar Osmanca 0000-0002-6939-2765

İ. Fatih Mercimek 0000-0002-2179-8803

Publication Date February 29, 2024
Submission Date April 27, 2022
Published in Issue Year 2024

Cite

APA 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, 27(1), 133-138. https://doi.org/10.2339/politeknik.1109101
AMA Yücel M, Osmanca MS, Mercimek İF. 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. February 2024;27(1):133-138. doi:10.2339/politeknik.1109101
Chicago Yücel, Murat, Mustafa Serdar Osmanca, and İ. Fatih Mercimek. “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 27, no. 1 (February 2024): 133-38. https://doi.org/10.2339/politeknik.1109101.
EndNote Yücel M, Osmanca MS, Mercimek İF (February 1, 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 27 1 133–138.
IEEE M. Yücel, M. S. Osmanca, and İ. F. Mercimek, “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, vol. 27, no. 1, pp. 133–138, 2024, doi: 10.2339/politeknik.1109101.
ISNAD Yücel, Murat et al. “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 27/1 (February 2024), 133-138. https://doi.org/10.2339/politeknik.1109101.
JAMA Yücel M, Osmanca MS, Mercimek İF. 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. 2024;27:133–138.
MLA Yücel, Murat et al. “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, vol. 27, no. 1, 2024, pp. 133-8, doi:10.2339/politeknik.1109101.
Vancouver Yücel M, Osmanca MS, Mercimek İF. 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. 2024;27(1):133-8.
 
TARANDIĞIMIZ DİZİNLER (ABSTRACTING / INDEXING)
181341319013191 13189 13187 13188 18016 

download Bu eser Creative Commons Atıf-AynıLisanslaPaylaş 4.0 Uluslararası ile lisanslanmıştır.