Qiuyue Li,
Difei Cao,
- Student Cambridge Institute of Technology, North Campus, China
- Student, Cambridge Institute of Technology, North Campus, China
Abstract
Investigating the channel quality of transmission (QoT) in-depth is crucial for responsive and instant transparent optical network management. However, current lightpath QoT predictions are devoted to univariate modeling. Due to the QoT metrics complicated time-varying process and the existing effects of inter-channel factors, despite the use of an advanced and efficient echo state network (ESN), it is difficult to obtain ideal results in univariate prediction mode. Thus, this paper considers a 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 hurt 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 to promptly and accurately detect and address optical link problems and provide transparent, real-time network intelligent management. Experimental results on real-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.
Keywords: Quality of Transmission; Optical backbone network; Multiple echo state network; multi-factor prediction
[This article belongs to Trends in Opto-electro & Optical Communication(toeoc)]
Browse Figures
References
- 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.
Trends in Opto-electro & Optical Communication
Volume | 14 |
Issue | 01 |
Received | April 23, 2024 |
Accepted | May 31, 2024 |
Published | August 8, 2024 |