[1] P. J. Winzer, D. T. Neilson, A. R. Chraplyvy. Fiber-optic transmission and networking: the previous 20 and the next 20 years [Invited]. Opt. Express, 26, 24190-24239(2018).

[2] B. Lee. Review of the present status of optical fiber sensors. Opt. Fiber Technol., 9, 57-79(2003).

[3] D. M. Chow et al. Distributed forward Brillouin sensor based on local light phase recovery. Nat. Commun., 9, 2990(2018).

[4] C. Pang et al. Opto-mechanical time-domain analysis based on coherent forward stimulated Brillouin scattering probing. Optica, 7, 176-184(2020).

[5] B. Warf. International competition between satellite and fiber optic carriers: a geographic perspective. Prof. Geogr., 58, 1-11(2006).

[6] Global_2021_Forecast_Highlights(2016).

[7] Cisco annual internet report - Cisco annual internet report (2018–2023) white paper(2020).

[8] J. Cho et al. Shaping lightwaves in time and frequency for optical fiber communication. Nat. Commun., 13, 785(2022).

[9] A. P. T. Lau et al. Advanced DSP techniques enabling high spectral efficiency and flexible transmissions: toward elastic optical networks. IEEE Signal Process. Mag., 31, 82-92(2014).

[10] C. E. Shannon. A mathematical theory of communication. Bell Syst. Tech. J., 27, 379-423(1948).

[11] A. Napoli et al. Towards multiband optical systems, NeTu3E.1(2018).

[12] R. G. H. Van Uden et al. Ultra-high-density spatial division multiplexing with a few-mode multicore fibre. Nat. Photonics, 8, 865-870(2014).

[13] M. Cantono et al. Opportunities and challenges of C+L transmission systems. J. Light. Technol., 38, 1050-1060(2020).

[14] D. Rafique, L. Velasco. Machine learning for network automation: overview, architecture, and applications [Invited tutorial]. J. Opt. Commun. Networking, 10, D126(2018).

[15] H. Zheng et al. From automation to autonomous: driving the optical network management to fixed fifth-generation (F5G) advanced, 385-389(2023).

[16] M. P. Yankov, U. C. de Moura, F. D. Ros. Power evolution modeling and optimization of fiber optic communication systems with EDFA repeaters. J. Light. Technol., 39, 3154-3161(2021).

[17] G. P. Agrawal. Nonlinear Fiber Optics(2001).

[18] E. Temprana et al. Overcoming Kerr-induced capacity limit in optical fiber transmission. Science, 348, 1445-1448(2015).

[19] A. R. Chraplyvy. Optical power limits in multi-channel wavelength-division-multiplexed systems due to stimulated Raman scattering. Electron. Lett., 20, 58-59(1984).

[20] S. Tariq, J. C. Palais. A computer model of non-dispersion-limited stimulated Raman scattering in optical fiber multiple-channel communications. J. Light. Technol., 11, 1914-1924(1993).

[21] D. Semrau, R. I. Killey, P. Bayvel. The Gaussian noise model in the presence of inter-channel stimulated Raman scattering. J. Light. Technol., 36, 3046-3055(2018).

[22] H. Buglia et al. An extended version of the ISRS GN model in closed-form accounting for short span lengths and low losses, 1-4(2022).

[23] A. K. Srivastava et al. EDFA transient response to channel loss in WDM transmission system. IEEE Photonics Technol. Lett., 9, 386-388(1997).

[24] Y. You, Z. Jiang, C. Janz. Machine learning-based EDFA gain model, 1-3(2018).

[25] F. da Ros, U. C. de Moura, M. P. Yankov. Machine learning-based EDFA gain model generalizable to multiple physical devices, Tu1A-4(2020).

[26] J. Yu et al. Machine-learning-based EDFA gain estimation [Invited]. Commun. Networking, 13, B83-B91(2021).

[27] Z. Jiang, J. Lin, H. Hu. Machine learning based EDFA channel in-band gain ripple modeling, W4I.2(2022).

[28] B. Pedersen et al. Experimental and theoretical analysis of efficient erbium-doped fiber power amplifiers. IEEE Photonics Technol. Lett., 3, 1085-1087(1991).

[29] R. Sommer et al. Multiple filter functions integrated into multi-port GFF components, 1-3(2007).

[30] L. Rapp, M. Eiselt. Optical amplifiers for multi–band optical transmission systems. J. Light. Technol., 40, 1579-1589(2022).

[31] A. A. M. Saleh et al. Modeling of gain in erbium-doped fiber amplifiers. IEEE Photonics Technol. Lett., 2, 714-717(1990).

[32] C. R. Giles, E. Desurvire. Modeling erbium-doped fiber amplifiers. J. Lightwave Technol., 9, 271-283(1991).

[33] M. Hashimoto, M. Yoshida, H. Tanaka. The characteristics of WDM systems with hybrid AGC EDFA in the photonics network, 517-518(2002).

[34] J. Junio, D. C. Kilper, V. W. S. Chan. Channel power excursions from single-step channel provisioning. J. Opt. Commun. Networking, 4, A1-A7(2012).

[35] K. Ishii, J. Kurumida, S. Namiki. Experimental investigation of gain offset behavior of feed forward-controlled WDM AGC EDFA under various dynamic wavelength allocations. IEEE Photonics J., 8, 7901713(2016).

[36] Z. Wang, D. Kilper, T. Chen. Transfer learning-based ROADM EDFA wavelength dependent gain prediction using minimized data collection, 1-3(2023).

[37] Y. You, Z. Jiang, C. Janz. OSNR prediction using machine learning-based EDFA models, 1-3(2019).

[38] Y. Liu et al. Modeling EDFA gain: approaches and challenges. Photonics, 8, 417(2021).

[39] K. Débicki, N. Balakrishnan et al. Gaussian process: overview. Wiley StatsRef: Statistics Reference Online(2014).

[40] K. Kandasamy, J. Schneider, B. Poczos. Bayesian active learning for posterior estimation, 3605-3611(2015).

[41] E. Brochu, V. M. Cora, N. de Freitas. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning(2010).

[42] J. Snoek, H. Larochelle, R. P. Adams. Practical Bayesian optimization of machine learning algorithms, 25(2012).

[43] P. I. Frazier. A tutorial on Bayesian optimization(2018).

[44] Z. Zhong et al. BOW: first real-world demonstration of a Bayesian optimization system for wavelength reconfiguration, F3B.1(2021).

[45] D. Zhan, H. Xing. Expected improvement for expensive optimization: a review. J. Global Optim., 78, 507-544(2020).

[46] A. Garivier, E. Moulines. On upper-confidence bound policies for switching bandit problems. Lect. Notes Comput. Sci., 6925, 174-188(2011).

[47] M. P. Yankov, F. Da Ros. Input-output power spectral densities for three C-band EDFAs and four multi-span inline EDFAd fiber optic systems of different lengths(2020).

[48] Z. Wang, D. C. Kilper, T. Chen. Open EDFA gain spectrum dataset and its applications in data-driven EDFA gain modeling. J. Opt. Commun. Networking, 15, 588(2023).

[49] S. Zhu et al. Hybrid machine learning EDFA model, T4B.4(2020).

[50] A. Ferrari et al. GNPy: an open source application for physical layer aware open optical networks. J. Opt. Commun. Networking, 12, C31-C40(2020).

[51] P. Poggiolini et al. The GN-model of fiber non-linear propagation and its applications. J. Lightwave Technol., 32, 694-721(2014).

[52] B. Neto et al. Efficient use of hybrid genetic algorithms in the gain optimization of distributed Raman amplifiers. Opt. Express, 15, 17520(2007).

[53] S. Singh, R. S. Kaler. Performance optimization of EDFA–Raman hybrid optical amplifier using genetic algorithm. Opt. Laser Technol., 68, 89-95(2015).

[54] J. Chen, H. Jiang. Optimal design of gain-flattened Raman fiber amplifiers using a hybrid approach combining randomized neural networks and differential evolution algorithm. IEEE Photonics J., 10, 7101915(2018).

[55] J. Zhou et al. Robust, compact, and flexible neural model for a fiber Raman amplifier. J. Lightwave Technol., 24, 2362-2367(2006).

[56] D. Zibar et al. Inverse system design using machine learning: the Raman amplifier case. J. Lightwave Technol., 38, 736-753(2020).

[57] X. Ye et al. Experimental prediction and design of ultra-wideband Raman amplifiers using neural networks, W1K.3(2020).

[58] G. Marcon et al. Model-aware deep learning method for Raman amplification in few-mode fibers. J. Lightwave Technol., 39, 1371-1380(2021).

[59] M. P. Yankov et al. Flexible Raman amplifier optimization based on machine learning-aided physical stimulated Raman scattering model. J. Lightwave Technol., 41, 508-514(2023).

[60] M. N. Islam. Raman amplifiers for telecommunications. IEEE J. Sel. Top. Quantum Electron., 8, 548-559(2002).

[61] C. M. Bishop. Pattern Recognition and Machine Learning, Information Science and Statistics(2006).

[62] D. P. Kingma, J. Ba. Adam: a method for stochastic optimization(2017).

[63]

[64] N. Stander, K. J. Craig. On the robustness of a simple domain reduction scheme for simulation-based optimization. Eng. Comput., 19, 431-450(2002).