[{“box”:0,”content”:”[if 992 equals=”Open Access”]n
n
Open Access
nn
n
n[/if 992]n
n
n
n
n
n
Qiuyue Li, Difei Cao
n
- n t
n
n
n[/foreach]
n
n[if 2099 not_equal=”Yes”]n
- [foreach 286] [if 1175 not_equal=””]n t
- Student,, Student, Department of Network and Information Security, Chongqing Vocational Institute of Safety Technology,, Department of Computer & Communication Engineering, University of Science and Technology Beijing, Chongqing,, Beijing, China., China.
n[/if 1175][/foreach]
n[/if 2099][if 2099 equals=”Yes”][/if 2099]n
Abstract
n: For responsive and instant transparent optical network management, investigating the channel quality of transmission (QoT) in-depth is crucial. However, current lightpath QoT predictions are devoted to uni-variate modeling. Due to the QoT metrics complicated time-varying process and the existing effects of inter-channel factors, despite the use of advanced and efficient echo state network (ESN), it is difficult to obtain ideal results in univariate prediction mode. Thus, in this paper, we considered multi-factor fused mode for lightpath performance prediction based in multiple ESN (MSEN). In fact, despite the optical backbone network’s ultra-high data rates, long-distance transmission networks are frequently subject to erratic and dynamic external factors that have a negative impact on service quality. As a result, network managers are forced to increase operating costs and add more design margins. Therefore, it is crucial to track the physical layer performance of optical networks in order to promptly and accurately detect and address optical link problems and provide transparent, real-time network intelligent management. Experimental results on re-al-world optical-layer characteristics from the Microsoft optical backbone network demonstrate that the proposed MESN performs much better in prediction than the classic ESN approaches.
n
Keywords: Quality of Transmission; Optical backbone network; Multiple echo state network; Multi-factor prediction
n[if 424 equals=”Regular Issue”][This article belongs to Trends in Opto-electro & Optical Communication(toeoc)]
n
n
n
n
n
nn[if 992 equals=”Open Access”] Full Text PDF Download[/if 992] n[if 992 not_equal=”Open Access”]
[/if 992]n[if 992 not_equal=”Open Access”] nnn[/if 992]nn[if 379 not_equal=””]n
Browse Figures
n
n[/if 379]n
References
n[if 1104 equals=””]n
- Shoaib, S. Shoaib, R. Y. Khattak, I. Shoaib, X. Chen, and A. Perwaiz, “Mimo antennas for smart 5g devices,” IEEE Access, vol. 6, pp. 77 014–77 021, 2018.
- Tzanakaki, M. P. Anastasopoulos, and D. Simeonidou, “Converged optical, wireless, and data center network infras-tructures for 5g services,” Journal of Optical Communications and Networking, vol. 11, no. 2, pp. 111–122, 2019.
- Ahmed, A. Mitra, S. Rahman, M. Tornatore, A. Lord, and B. Mukherjee, “C+ l-band upgrade strategies to sustain traffic
growth in optical backbone networks,” Journal of Optical Communications and Networking, vol. 13, no. 7, pp. 193–203, 2021.
- Wang, H. Jiang, G. Liang, Q. Zhan, Y. Mo, Q. Sui, and Z. Li, “Optical performance monitoring of multiple parameters in future optical networks,” Journal of Lightwave Technology, vol. 39, no. 12, pp. 3792–3800, 2021.
- Bergk, B. Shariati, P. Safari, and J. K. Fischer, “Ml-assisted qot estimation: a dataset collection and data visualization for dataset quality evaluation,” Journal of Optical Communications and Networking, vol. 14, no. 3, pp. 43–55, 2022.
- Mahajan, K. Christodoulopoulos, R. Martínez, S. Spadaro, and R. Muñoz, “Modeling edfa gain ripple and filter penalties with machine learning for accurate qot estimation,” Journal of Lightwave Technology, vol. 38, no. 9, pp. 2616–2629, 2020.
- Ibrahimi, H. Abdollahi, C. Rottondi, A. Giusti, A. Ferrari, V. Curri, and M. Tornatore, “Machine learning regression for qot estimation of unestablished lightpaths,” Journal of Optical Communications and Networking, vol. 13, no. 4, pp. 92–101, 2021.
- Khan, M. Bilal, M. U. Masood, A. D’Amico, and V. Curri, “Lightpath qot computation in optical networks assisted by
transfer learning,” Journal of Optical Communications and Networking, vol. 13, no. 4, pp. 72–82, 2021.
- Barzegar, M. Ruiz, A. Sgambelluri, F. Cugini, A. Napoli, and L. Velasco, “Soft-failure detection, localization, identification, and severity prediction by estimating qot model input parameters,” IEEE Transactions on Network and Service Management, vol. 18, no. 3, pp. 2627–2640, 2021.
- Li, J. Wang, D. Cai, Z. Li, D. Fu, and L. Qin, “Towards high-efficient QoT prediction of wide-area optical backbone network: A reservoir computing view,” in 2020 IEEE/CIC International Conference on Communications in China (ICCC), 2020: IEEE, pp. 384-388.
- Habibi and H. Beyranvand, “Impairment-aware manycast routing, modulation level, and spectrum assignment in elastic optical networks,” Journal of Optical Communications and Networking, vol. 11, no. 5, pp. 179–189, 2019.
- Li, F. Zhang, L. Zhang, X. Chen, F. Yang, H. Ming, X. Ruan, Z. Chen, and C. Yang, “Inter-channel fiber non-linearity mitigation in high baud-rate optical communication systems,” Journal of Lightwave Technology, vol. 39, no. 6, pp. 1653–1661, 2020.
- Matsushita, M. Nakamura, S. Yamamoto, F. Hamaoka, and Y. Kisaka, “41-tbps c-band wdm transmission with 10-bps/hz spectral efficiency using 1-tbps/λ signals,” Journal of Lightwave Technology, vol. 38, no. 11, pp. 2905–2911, 2020.
- Ghobadi and R. Mahajan, “Optical layer failures in a large backbone,” in Proceedings of the 2016 Internet Measurement
Conference, 2016, pp. 461–467.
nn[/if 1104][if 1104 not_equal=””]n
- [foreach 1102]n t
- [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
n[/foreach]
n[/if 1104]
nn
nn[if 1114 equals=”Yes”]n
n[/if 1114]
n
n
n
n
n
n
n
| Volume | 14 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | April 23, 2024 | |
| Accepted | May 31, 2024 | |
| Published | June 14, 2024 |
n
n
n
n
n
n function myFunction2() {n var x = document.getElementById(“browsefigure”);n if (x.style.display === “block”) {n x.style.display = “none”;n }n else { x.style.display = “Block”; }n }n document.querySelector(“.prevBtn”).addEventListener(“click”, () => {n changeSlides(-1);n });n document.querySelector(“.nextBtn”).addEventListener(“click”, () => {n changeSlides(1);n });n var slideIndex = 1;n showSlides(slideIndex);n function changeSlides(n) {n showSlides((slideIndex += n));n }n function currentSlide(n) {n showSlides((slideIndex = n));n }n function showSlides(n) {n var i;n var slides = document.getElementsByClassName(“Slide”);n var dots = document.getElementsByClassName(“Navdot”);n if (n > slides.length) { slideIndex = 1; }n if (n (item.style.display = “none”));n Array.from(dots).forEach(n item => (item.className = item.className.replace(” selected”, “”))n );n slides[slideIndex – 1].style.display = “block”;n dots[slideIndex – 1].className += ” selected”;n }n”}]