AI-based prediction of transmission quality in cognitive optical networks
-
Shankar M. Patil
,
Shilpa M. Satre
,
Gurunath T. Chavan
Abstract
Quality of Transmission (QoT) prediction is done by a proposed method in optical networks. It uses a Radial Basis Function Network (RBFN) model trained with data from a comprehensive optical model. The RBFN model is enhanced with several techniques to improve its accuracy. The key objective is to enhance hardware utilization by significantly reducing the required system margin, potentially up to the order of dBs. To achieve this, the study employs the Radial Basis Function Network (RBFN) model, capitalizing on input data related to connectivity and signal characteristics for QoT prediction. The proposed method achieves good performance (MSE: 0.802, MAE: 0.2) but is slower than some existing methods. However, compared to these existing methods, the proposed method has 1.54 %, 5.32 %, and 5.46 % higher performance than SOM-RBF, AHFSE, and Wavelet-chaos NN. This research also contributes to the field by introducing a new cognitive-based QoT model that uses deep learning techniques. The study showcases the potential for practical implementation and optimization in relevant applications, emphasizing the intersection of artificial intelligence and optical network resource utilization.
Acknowledgments
Not applicable.
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: All authors contributed to the design and implementation of the research, to the analysis of the results, and to the writing of the manuscript.
-
Use of Large Language Models, AI and Machine Learning Tools: None declared.
-
Conflict of interest: Not applicable.
-
Research funding: Not applicable.
-
Data availability: Not applicable.
References
1. Singh, H, Srivastava, DD. Sentiment analysis: quantitative evaluation of machine learning algorithms. In: Proceedings of the 5th international conference on smart systems and inventive technology (ICSSIT), Tirunelveli, India; 2023. DVD Part Number: CFP23P17-DVD; ISBN: 978-1-6654-7466-5.Suche in Google Scholar
2. Usmani, F, Khan, I, Masood, MU, Ahmad, A, Shahzad, M, Curri, V. Convolutional neural network for quality of transmission prediction of unestablished lightpaths. Microw Opt Technol Lett 2021;63:2461–9. https://doi.org/10.1002/mop.32996.Suche in Google Scholar
3. Komathi, M, Pushpa Rani, A. Trust performance of AODV, DSR and DSDV in wireless sensor networks. In: IEEE - Second International Conference on Current Trends In Engineering and Technology-ICCTET; 2014:423–5 pp. 2014/7/8.10.1109/ICCTET.2014.6966330Suche in Google Scholar
4. Singh, H, Ramya, D, Saravanakumar, R, Sateesh, N, Anand, R, Singh, S, et al.. Artificial intelligence based quality of transmission predictive model for cognitive optical networks. Optik 2022;257:168789. https://doi.org/10.1016/j.ijleo.2022.168789.Suche in Google Scholar
5. Ibrahimi, M, Rottondi, C, Tornatore, M. Machine learning methods for quality-of-transmission estimation. In: Lau APT, Khan FN, editors. Machine Learning for future fiber-optic communication systems, 1st ed. Academic Press; 2022:189–224 pp.10.1016/B978-0-32-385227-2.00014-0Suche in Google Scholar
6. Lokesh Khedekar, S, Prajakta Kale, S. Strength of QR code over design and implementation of authentication system. In: International Conference on Communication and Signal Processing (ICCSP); 2016.10.1109/ICCSP.2016.7754571Suche in Google Scholar
7. Thangaraj, J, Barreto, AAD, Dias Barreto, AA. Accurate QoT estimation for the optimized design of optical transport network based on advanced deep learning model. Opt Fiber Technol 2022;70:102895. https://doi.org/10.1016/j.yofte.2022.102895.Suche in Google Scholar
8. Qin, D. Performance analysis of cognitive hybrid radio frequency and free space optical cooperative networks. Comput Electr Eng 2022;100:107907. https://doi.org/10.1016/j.compeleceng.2022.107907.Suche in Google Scholar
9. Radhiya Devi, C, Jayanthi, SK. DCNMAF: dilated convolution neural network model with mixed activation functions for image de-noising. Int J Intell Syst Appl Eng 2023;11:552–7.10.17762/ijritcc.v11i10.8955Suche in Google Scholar
10. Karthik, S, Anupama, AS, Deekshith, SA, Santhosh, L, Dhanraj, M. Crypto AI: digital nostalgic art generation using GAN and creation of NFT using Blockchain. J Emerg Technol Innov Res 2024;9:217–20.Suche in Google Scholar
11. Pointurier, Y. Machine learning techniques for quality of transmission estimation in optical networks. J Opt Commun Netw 2021;13:B60–71. https://doi.org/10.1364/jocn.417434.Suche in Google Scholar
12. Fu, Y, Chen, J, Wu, W, Huang, Y, Hong, J, Chen, L, et al.. A QoT prediction technique based on machine learning and NLSE for QoS and new lightpaths in optical communication networks. Front Optoelectron 2021;14:513–21. https://doi.org/10.1007/s12200-020-1079-y.Suche in Google Scholar PubMed PubMed Central
13. Díaz-Montiel, AA, Lantz, B, Yu, J, Kilper, D, Ruffini, M. Real-time QoT estimation through SDN control e monitoring evaluated in mininet-optical. IEEE Photon Technol Lett 2021;33:1050–3. https://doi.org/10.1109/lpt.2021.3075277.Suche in Google Scholar
14. Khan, I, Bilal, M, Masood, MU, D’Amico, A, Curri, V. Lightpath QoT computation in optical networks assisted by transfer learning. J Opt Commun Netw 2021;13:B72–82. https://doi.org/10.1364/jocn.409538.Suche in Google Scholar
15. Mahajan, A, Christodoulopoulos, KK, Martínez, R, Muñoz, R, Spadaro, S. Quality of transmission estimator retraining for dynamic optimization in optical networks. J Opt Commun Netw 2021;13:B45–9. https://doi.org/10.1364/jocn.411524.Suche in Google Scholar
16. Rottondi, C, di Marino, R, Nava, M, Giusti, A, Bianco, A. On the benefits of domain adaptation techniques for quality of transmission estimation in optical networks. J Opt Commun Netw 2021;13:A34–3. https://doi.org/10.1364/jocn.401915.Suche in Google Scholar
17. Vejdannik, M, Sadr, A. Modular neural networks for quality of transmission prediction in low-margin optical networks. J Intell Manuf 2021;32:361–75. https://doi.org/10.1007/s10845-020-01576-z.Suche in Google Scholar
18. Savva, G, Panayiotou, T, Tomkos, I, Ellinas, G. Deep graph learning for QoT estimation of unseen optical sub-network states: capturing the crosstalk impact on the in-service lightpaths. J Lightwave Technol 2021;40:921–34. https://doi.org/10.1109/jlt.2021.3129646.Suche in Google Scholar
19. Cruzes, S. Overview of quality of transmission estimation in optical networks. In: Authorea preprints. international conference on optical network design and modeling (ONDM). IEEE, TechRxiv; 2024:1–3 pp.10.36227/techrxiv.171340665.50082312/v1Suche in Google Scholar
20. Khan, I, Bilal, M, Curri, V. Assessment of cross-train machine learning techniques for QoT-estimation in agnostic optical networks. OSA Continuum 2020a;3:2690–706. https://doi.org/10.1364/osac.399511.Suche in Google Scholar
21. S Yalçın, and H Vural. Fault detection framework with digital twin based on artificial intelligence models for wireless networks.Suche in Google Scholar
22. Zhu, S. Machine learning enhanced quality of transmission estimation in disaggregated optical systems Doctoral dissertation. The University of Arizona; 2020.Suche in Google Scholar
23. Mahajan, A, Christodoulopoulos, K, Martinez, R, Spadaro, S, Munoz, R. Modeling filtering penalties in ROADM-based networks with machine learning for QoT estimation. In: . Optical fiber communication conference. Optica Publishing Group; 2020. Th3D-4.10.1364/OFC.2020.Th3D.4Suche in Google Scholar
24. H Abdollahi. Machine learning-based regression vs. classification for quality of transmission (QoT) estimation of unestablished lightpaths. (2020).Suche in Google Scholar
25. Pesic, J, Lonardi, M, Rossi, N, Zami, T, Seve, E, Pointurier, Y. How uncertainty on the fiber span lengths influences QoT estimation using machine learning in WDM networks. In: Optical fiber communication conference. Optica Publishing Group; 2020. Th3D-5.10.1364/OFC.2020.Th3D.5Suche in Google Scholar
26. Mahadevappa, P, Al-amri, R, Alkawsi, G, Alkahtani, AA, Alghenaim, MF, Alsamman, M. Analyzing threats and attacks in edge data analytics within IoT environments. IoT 2024;5:123–54. https://doi.org/10.3390/iot5010007.Suche in Google Scholar
27. Khan, I, Bilal, M, Siddiqui, M, Khan, M, Ahmad, A, Shahzad, M, et al.. QoT estimation for light-path provisioning in un-seen optical networks using machine learning. In: 2020 22nd international conference on transparent optical networks (ICTON). IEEE: Bari, Italy; 2020b:1–4 pp.10.1109/ICTON51198.2020.9203364Suche in Google Scholar
28. Aladin, S, Tran, AVS, Allogba, S, Tremblay, C. Quality of transmission estimation and short-term performance forecast of lightpaths. J Lightwave Technol 2020;38:2807–14. https://doi.org/10.1109/jlt.2020.2975179.Suche in Google Scholar
29. Balshetwar, SV, RS, A, DJ, R. Fake news detection in social media based on sentiment analysis using classifier techniques. Multimed Tool Appl 2023;82:35781–811. https://doi.org/10.1007/s11042-023-14883-3.Suche in Google Scholar PubMed PubMed Central
30. Wadapurkar, R, Bapat, S, Mahajan, R, Vyas, R. Machine learning approaches for prediction of ovarian cancer driver genes from mutational and network analysis. Data Technol Appl 2023. https://doi.org/10.1108/dta-03-2022-0096.Suche in Google Scholar
31. Faritha Banu, J, Atul Mahajan, R, Sakthi, U, Kumar Nassa, V, Lakshmi, D, Nadanakumar, V. Artificial intelligence with attention based BiLSTM for energy storage system in hybrid renewable energy sources. Sustain Energy Technol Assessments 2022;52:102334. https://doi.org/10.1016/j.seta.2022.102334.Suche in Google Scholar
32. Qadeer, SA, Khan, MYNS, Nath, V. High resolution fuel indicating and tracking system. Microsyst Technol 2019;25:2267–71. https://doi.org/10.1007/s00542-018-4107-8.Suche in Google Scholar
33. Kumar, M, Mishra, SK, Joseph, J, Jangir, SK, Goyal, D. Adaptive comprehensive particle swarm optimisation-based functional-link neural network filter model for denoising ultrasound images. IET Image Process;15:1232–46. https://doi.org/10.1049/ipr2.12100.Suche in Google Scholar
34. Kumar Mishra, G, Mohanty, NK, Panda, AB, Kumar Pradhan, P, Pandey, S, Behera, B. Structural and dielectric properties of GdFeAsO ceramic material. Chem Phy Imp 2023;7:100276. Direct link to article https://doi.org/10.1016/j.chphi.2023.100276.Suche in Google Scholar
35. Kaur, P, Gupta, A. Real-time battery monitoring and predictive maintenance for electric scooters using IOT technology. Int J Adv Res Eng Technol 2019;10:846–55.Suche in Google Scholar
36. Botla, G, Barmavatu, P, Pohorely, M, Jeremias, M, Singh Sikarwar, V. Optimization of value-added products using response surface methodology from the HDPE waste plastic by thermal cracking. Thermal science and engineering progress. Elsevier Publishers; 2024, 50:102514 p. https://www.sciencedirect.com/science/article/pii/S245190492400132X.10.1016/j.tsep.2024.102514Suche in Google Scholar
37. He, H, Bai, Y, Garcia, EA, Adasyn, SL. Adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE international joint conference on neural networks: IEEE world congress on computational intelligence), Hong Kong: IEEE; 2008:1322–8 pp.10.1109/IJCNN.2008.4633969Suche in Google Scholar
© 2025 Walter de Gruyter GmbH, Berlin/Boston
