Predicting failures in fiber optic information transmission systems with support of machine learning

Nafisa Juraeva; Dilmurod Davronbekov; Ulugbek Turdiev

doi:10.51252/rcsi.v5i2.907

Authors

Nafisa Juraeva Tashkent University of Information Technology
Dilmurod Davronbekov Tashkent University of Information Technology
Ulugbek Turdiev University of Information Technologies and Management

DOI:

https://doi.org/10.51252/rcsi.v5i2.907

Keywords:

extra tree regressor, failure prediction, machine learning, random forest, regression algorithms, support vector regression

Abstract

The use of machine learning methods in fiber-optic information transmission systems (FOITS) is considered. The article discusses the basic operating principles of fiber optic systems and the problems they face, such as noise, nonlinear effects, and degradation of transmitted information. Describes various machine learning techniques used in FOITS to control and monitor performance, prevent intelligent decisions, and suppress nonlinear fiber optic noise. Approaches used in machine learning are presented, such as neural networks, classification and regression algorithms, their application in the analysis and optimization of FOITS, such as neural networks, support vector machines, classification and regression algorithms, their application in the analysis and optimization of fiber optic systems. This paper proposes a method for monitoring performance and predicting failures in optical networks based on machine learning. The results obtained allow us to draw conclusions about the most effective methods for predicting failures, which is of great practical importance for ensuring the reliability of communication networks and minimizing downtime.

References

Araújo, T., Silva, L., Aguiar, A., & Moreira, A. (2023). Calibration Assessment of Low-Cost Carbon Dioxide Sensors Using the Extremely Randomized Trees Algorithm. Sensors, 23(13), 6153. https://doi.org/10.3390/s23136153 DOI: https://doi.org/10.3390/s23136153

Berghmans, F., Eve, S., & Held, M. (2008). An Introduction to Reliability of Optical Components and Fiber Optic Sensors. En W. J. Bock, I. Gannot, & S. Tanev (Eds.), Optical Waveguide Sensing and Imaging (pp. 73-100). Springer Netherlands. https://doi.org/10.1007/978-1-4020-6952-9_4 DOI: https://doi.org/10.1007/978-1-4020-6952-9_4

Davronbekov, D. (2016). Features measurement parameters and control functioning of integrated chips. 2016 International Conference on Information Science and Communications Technologies (ICISCT), 1-3. https://doi.org/10.1109/ICISCT.2016.7777379 DOI: https://doi.org/10.1109/ICISCT.2016.7777379

Davronbekov, D. A., & Juraeva, N. I. (2022). Features and Principle of Operation of Fiber Lasers based on Active Fiber Doped with Rare-Earth Ions. 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), 348-352. https://doi.org/10.1109/ICTACS56270.2022.9988280 DOI: https://doi.org/10.1109/ICTACS56270.2022.9988280

Davronbekov, D. A., & Juraeva, N. I. (2023). Study of the features of fiber-optic amplifiers used on extended communication lines. Bulletin of TUIT: Management and Communication Technologies, 1(4). https://doi.org/10.61663/tuit23.1.5 DOI: https://doi.org/10.61663/tuit23.1.5

Davronbekov, D. A., Pisetskiy, U. V., & Urazov, T. A. (2014). Diagnostic methods fiber optic communication lines. Transactions of the International Scientific Conference “Perspectives for the Development of Information Technologies ITPA 2014”, 283–286.

Davronbekov, D., Juraeva, N., & Boboev, A. (2024). Advanced Applications of Machine Learning Techniques in FOITS. 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS), 773-778. https://doi.org/10.1109/ICTACS62700.2024.10840715 DOI: https://doi.org/10.1109/ICTACS62700.2024.10840715

Dikbiyik, F., Tornatore, M., & Mukherjee, B. (2014). Minimizing the Risk From Disaster Failures in Optical Backbone Networks. Journal of Lightwave Technology, 32(18), 3175-3183. https://opg.optica.org/jlt/abstract.cfm?uri=jlt-32-18-3175 DOI: https://doi.org/10.1109/JLT.2014.2334713

Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3-42. https://doi.org/10.1007/s10994-006-6226-1 DOI: https://doi.org/10.1007/s10994-006-6226-1

Gordienko, V. N., Korshunov, V. N., & Shishova, N. A. (2016). Increasing the speed of fiber-optic transmission systems. Electrosvyaz, 5, 27-33.

Gu, R., Yang, Z., & Ji, Y. (2020). Machine learning for intelligent optical networks: A comprehensive survey. Journal of Network and Computer Applications, 157, 102576. https://doi.org/10.1016/j.jnca.2020.102576 DOI: https://doi.org/10.1016/j.jnca.2020.102576

Hakimov, Z. T., & Davronbekov, D. A. (2007). Equalization of spectral characterist of optical signanls by acousto-optic filters. 2007 3rd IEEE/IFIP International Conference in Central Asia on Internet, 1-3. https://doi.org/10.1109/CANET.2007.4401704 DOI: https://doi.org/10.1109/CANET.2007.4401704

Huang, S., Li, X., Yin, S., Guo, B., Zhao, Y., Zhang, J., Zhang, M., & Gu, W. (2016). Shared end-to-content backup path protection in k-node (edge) content connected elastic optical datacenter networks. Optics Express, 24(9), 9446. https://doi.org/10.1364/OE.24.009446 DOI: https://doi.org/10.1364/OE.24.009446

Khan, F. N., Fan, Q., Lu, C., & Lau, A. P. T. (2022). Introduction to machine learning techniques: An optical communication’s perspective. En Machine Learning for Future Fiber-Optic Communication Systems (pp. 1-42). Elsevier. https://doi.org/10.1016/B978-0-32-385227-2.00008-5 DOI: https://doi.org/10.1016/B978-0-32-385227-2.00008-5

Lu, J., Zhou, G., Fan, Q., Zeng, D., Guo, C., Lu, L., Li, J., Xie, C., Lu, C., Khan, F. N., & Lau, A. P. T. (2021). Performance comparisons between machine learning and analytical models for quality of transmission estimation in wavelength-division-multiplexed systems [Invited]. Journal of Optical Communications and Networking, 13(4), B35. https://doi.org/10.1364/JOCN.410876 DOI: https://doi.org/10.1364/JOCN.410876

Musumeci, F., Rottondi, C., Corani, G., Shahkarami, S., Cugini, F., & Tornatore, M. (2019). A Tutorial on Machine Learning for Failure Management in Optical Networks. Journal of Lightwave Technology, 37(16), 4125-4139. https://doi.org/10.1109/JLT.2019.2922586 DOI: https://doi.org/10.1109/JLT.2019.2922586

Nazarov, A., Ochilov, B., & Khasanov, M. (2021). Performance Characteristics of Fiber Optical Lines and Diagnostic Techniques for Optical Fiber Cable. 2021 International Conference on Information Science and Communications Technologies (ICISCT), 1-5. https://doi.org/10.1109/ICISCT52966.2021.9670045 DOI: https://doi.org/10.1109/ICISCT52966.2021.9670045

Rausand, M., Barros, A., & Hoyland, A. (2020). System Reliability Theory. Wiley. https://doi.org/10.1002/9781119373940 DOI: https://doi.org/10.1002/9781119373940

Saleh, H., & Layous, J. A. (2022). Machine Learning - Regression [Department of Electronic and Mechanical Systems]. https://doi.org/10.13140/RG.2.2.35768.67842

Senior, J. (2008). Optical Fiber Communications: Principles and Practice (3.a ed.). PEARSON.

Tran, Q., Nguyen, H., & Bui, X. (2023). Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models. Computer Modeling in Engineering & Sciences, 134(3), 2227–2246. https://loginom.ru/blog/quality-metrics DOI: https://doi.org/10.32604/cmes.2022.021893

Wang, D., Wang, D., Zhang, C., Wang, L., Liu, S., & Zhang, M. (2021a). A Review of Machine Learning-based Failure Management in Optical Networks. 26th Optoelectronics and Communications Conference, T3A.3. https://doi.org/10.1364/OECC.2021.T3A.3

Wang, D., Wang, D., Zhang, C., Wang, L., Liu, S., & Zhang, M. (2021b). Machine Learning for Optical Layer Failure Management. 26th Optoelectronics and Communications Conference, T3A.3. https://doi.org/10.1364/OECC.2021.T3A.3 DOI: https://doi.org/10.1364/OECC.2021.T3A.3

Weigang, H., Guo, L., Yu, C., & Zong, Y. (2016). Risk-aware virtual network embedding in optical data center networks. 2016 21st OptoElectronics and Communications Conference (OECC) held jointly with 2016 International Conference on Photonics in Switching (PS), 1-3. https://ieeexplore.ieee.org/document/7718481

Xujamatov, X. E. (2024). IoT asosida telekommunikatsiya qurilmalari gibrid energiya ta’minoti manbalarini avtomatlashtirilgan monitoring model va algoritmlari.