Design and performance evaluation of DWDM networks using reconfiguration and modulation methods
-
Deepthi Prakash
,
Manjunath Managuli
Abstract
In optical fibers, erbium-doped fiber amplifiers (EDFA) can amplify signals at different wavelengths, thus improving network connectivity, bandwidth requirements, and reduced transmission capacity. As the communication expanse and numeral of stations upturn, fiber nonlinearity and dispersion effects limit the efficiency of (DWDM) dense wavelength division multiplexing systems. This paper supports learning-based dense wavelength division multiplexing network reconfiguration (RLR), modulation-ultra-dense wavelength division multiplexing (UDWDM) differential quadrature phase shift keying (QPSK) to improve transmission, which has no positive effect in most cases. Configure redundancy at limited alteration and worldwide system level for better presentation. With new modifications (such as wavelength selection, modulation transformation, path planning, bandwidth transformation, etc.) according to previous studies, this work provides a comprehensive combination to support strategy selection. MatLab and OptiSystem simulator work together to simulate solutions.
Acknowledgments
Deepthi Prakash acknowledges the laboratory facility provided by K L S Gogte Institute of Technology, Belagavi, India.
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: Deepthi Prakash, Manjunath Managuli: Conceptualization, experimental and manuscript writing. Pavan Kunchur, Gururaj Kulkarni: Validation and finalization of the manuscript.
-
Use of Large Language Models, AI and Machine Learning Tools: None declared.
-
Conflict of interest: The author(s) state(s) no conflict of interest.
-
Research funding: None declared.
-
Data availability: Not applicable.
References
1. Morais, RM, Pedro, J. Machine learning models for estimating quality of transmission in DWDM networks. IEEE/OSA J Opt Commun Netw 2018;10:D84–99. https://doi.org/10.1364/jocn.10.000d84.Search in Google Scholar
2. Mo, W, Gutterman, CL, Li, Y, Zhu, S, Zussman, G, Kilper, DC. Deep-neural-network-based wavelength selection and switching in ROADM systems. IEEE/OSA J Opt Commun Netw 2018;10:D1–11. https://doi.org/10.1364/jocn.10.0000d1.Search in Google Scholar
3. Mestres, A, Rodriguez-Natal, A, Carner, J, Barlet-Ros, P, Alarcón, E, Solé, M, et al.. Knowledge-defined networking. SIGCOMM Comput Commun Rev 2017;47:2–10. https://doi.org/10.1145/3138808.3138810.Search in Google Scholar
4. Musumeci, F, Rottondi, C, Nag, A, Macaluso, I, Zibar, D, Ruffini, M, et al.. An overview on application of machine learning techniques in optical networks. IEEE Commun Surv Tutor. 2019;21:1383–408. https://doi.org/10.1109/comst.2018.2880039.Search in Google Scholar
5. Panayiotou, T, Savva, G, Shariati, B, Tomkos, I, Ellinas, G. Machine learning for QoT estimation of unseen optical network states. In: Proceedings of the 2019 optical fiber communications conference and exhibition (OFC). San Diego, CA, USA; 2019. p. 1–3. 20.10.1364/OFC.2019.Tu2E.2Search in Google Scholar
6. Allogba, S, Tremblay, CK. Nearest neighbors classifier for field bit error rate data. In:.Proceedings of the 2018 Asia communications and photonics conference (ACP). Hangzhou, China; 2018. p. 1–3. 21.10.1109/ACP.2018.8596133Search in Google Scholar
7. Alvizu, R, Troia, S, Maier, G, Pattavina, A. Matheuristic with machine-learning-based prediction for software-defined mobile metro-core networks. IEEE/OSA J Opt Commun Netw 2017;9:D19–30. https://doi.org/10.1364/jocn.9.000d19.Search in Google Scholar
8. Barletta, L, Giusti, A, Rottondi, C, Tornatore, M. QoT estimation for unestablished lightpaths using machine learning. In: Proceedings of the 2017 optical fiber communications conference and exhibition (OFC). Los Angeles, CA, USA; 2017. p. 1–3. 23.10.1364/OFC.2017.Th1J.1Search in Google Scholar
9. Bouda, M, Oda, S, Vasilieva, O, Miyabe, M, Yoshida, S, Katagiri, T, et al.. Accurate prediction of quality of transmission with dynamically configurable optical impairment model. In: Proceedings of the optical fiber communication conference. Los Angeles, LA, USA: Optical Society of America; 2017. Th1J.4 p.10.1364/OFC.2017.Th1J.4Search in Google Scholar
10. Mata, J, de Miguel, I, Durán, RJ, Aguado, JC, Merayo, N, Ruiz, L, et al.. A SVM approach for lightpath QoT estimation in optical transport networks. In: Proceedings of the 2017 IEEE international conference on big data (Big Data). Boston, MA, USA; 2017. 4795–7. p10.1109/BigData.2017.8258545Search in Google Scholar
11. Aladin, S, Tremblay, C. Cognitive tool for estimating the QoT of new lightpaths. In: Proceedings of the 2018 optical fiber communications conference and exposition (OFC). San Diego, CA, USA; 2018. 1–3 p.10.1364/OFC.2018.M3A.3Search in Google Scholar
12. Morais, RM, Pedro, J. Evaluating machine learning models for QoT estimation. In: Proceedings of the 2018 20th international conference on transparent optical networks (ICTON). Bucharest, Romania; 2018. 1–4 p.10.1109/ICTON.2018.8473941Search in Google Scholar
13. Rottondi, C, Barletta, L, Giusti, A, Tornatore, M. Machine-learning method for quality of transmission prediction of unestablished lightpaths. IEEE/OSA J Opt Commun Netw 2018;10:A286–97. https://doi.org/10.1364/jocn.10.00a286.Search in Google Scholar
14. Zhu, Y, Jiang, M, Chen, Z, Zhang, F. Terabit faster-than-nyquist PDM 16-QAM WDM transmission with a net spectral efficiency of 7.96 b/s/Hz. J Lightwave Technol 2018;36:2912–19. https://doi.org/10.1109/JLT.2018.2829341.Search in Google Scholar
15. Xu, C, Gao, G, Chen, S, Zhang, J. Pulse-overlapping super-nyquist WDM system. J Lightwave Technol 2018;36:3941–8. https://doi.org/10.1109/JLT.2018.2833120.Search in Google Scholar
16. Ghazisaeidi, A, Tran, P, Brindel, P, Bertran-Pardo, O, Renaudier, J, Charlet, G, et al.. Impact of tight optical filtering on the performance of 28 Gbaud NyquistWDM PDM-8QAM over 37.5 GHz grid. In: Optical fiber communication conference/national fiber optic engineers conference 2013, OSA technical digest (Online). Optical Society of America; 2013.10.1364/OFC.2013.OTu3B.6Search in Google Scholar
17. Yu, J, Dong, Z, Chien, H, Jia, Z, Li, X, Huo, D, et al.. Transmission of 200 G PDM-CSRZ-QPSK and PDM-16 QAM with a SE of 4 b/s/Hz. J Lightwave Technol 2013;31:515–22. https://doi.org/10.1109/JLT.2012.2212420.Search in Google Scholar
18. Morea, A, Renaudier, J, Ghazisaeidi, A, Bertran-Pardo, O, Zami, T. Impact of reducing channel spacing from 50GHz to 37.5GHz in fully transparent meshed networks. In: OFC 2014. San Francisco, CA; 2014:1–3 pp.10.1364/OFC.2014.Th1E.4Search in Google Scholar
19. A Morea, J Renaudier, T Zami, A Ghazisaeidi, O Bertran-Pardo, Throughput comparison between 50-GHz and 37.5-GHz grid transparent networks [Invited], IEEE/OSA J Opt Commun Netw 7 (2015), https://doi.org/10.1364/JOCN.7.00A293.A293-A300, 2015.Search in Google Scholar
20. Prakash, D, Managuli, M. Spectrally efficient DWDM system using DQPSK modulation. J Opt Commun 2024. https://doi.org/10.1515/joc-2024-0163.Search in Google Scholar
21. Prakash, D, Managuli, M. An optimization approach to DWDM network reconfiguration through reinforcement learning. SN Comput Sci 2024;5:1069. https://doi.org/10.1007/s42979-024-03438-4.Search in Google Scholar
© 2025 Walter de Gruyter GmbH, Berlin/Boston
