[1] Wong E, Mondal S, Ruan L H. Machine learning enhanced next-generation optical access networks: challenges and emerging solutions [invited tutorial][J]. Journal of Optical Communications and Networking, 15, A49-A62(2023).

[2] CISCO. Cisco annual internet report-Cisco annual internet report[EB/OL], 2018(2023). https:∥www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html

[3] Sun C Y, Yang X, Cicco N D et al. First experimental demonstration of full lifecycle automation of optical network through fine-tuned LLM and digital twin[EB/OL]. https:∥www.eurecom.edu/en/publication/7882

[4] Wang D S, Wang Y D, Jiang X T et al. When large language models meet optical networks: paving the way for automation[J]. Electronics, 13, 2529(2024).

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

[6] Gerstel O, Jinno M, Lord A et al. Elastic optical networking: a new dawn for the optical layer?[J]. IEEE Communications Magazine, 50, s12-s20(2012).

[7] Channegowda M, Nejabati R, Simeonidou D. Software-defined optical networks technology and infrastructure: Enabling software-defined optical network operations [invited][J]. Journal of Optical Communications and Networking, 5, A274-A282(2013).

[8] Bhaumik P, Zhang S Q, Chowdhury P et al. Software-defined optical networks (SDONs): a survey[J]. Photonic Network Communications, 28, 4-18(2014).

[9] López V, Velasco L[M]. Elastic optical networks(2016).

[10] Sambo N, Ferrari A, Napoli A et al. Provisioning in multi-band optical networks[J]. Journal of Lightwave Technology, 38, 2598-2605(2020).

[11] Andriolli N, Giorgetti A, Castoldi P et al. Optical networks management and control: a review and recent challenges[J]. Optical Switching and Networking, 44, 100652(2022).

[12] Casellas R, Martínez R, Vilalta R et al. Control, management, and orchestration of optical networks: evolution, trends, and challenges[J]. Journal of Lightwave Technology, 36, 1390-1402(2018).

[13] Xu Y X, Agrell E, Brandt-Pearce M. Cross-layer static resource provisioning for dynamic traffic in flexible grid optical networks[J]. Journal of Optical Communications and Networking, 13, 1-13(2021).

[14] Yadav D S, Babu S, Manoj B S. Quasi path restoration: a post-failure recovery scheme over pre-allocated backup resource for elastic optical networks[J]. Optical Fiber Technology, 41, 139-154(2018).

[15] Zhuge Q B, Liu X M, Zhang Y H et al. Building a digital twin for intelligent optical networks [invited tutorial][J]. Journal of Optical Communications and Networking, 15, C242-C262(2023).

[16] Wang D S, Song Y C, Zhang Y et al. Digital twin of optical networks: a review of recent advances and future trends[J]. Journal of Lightwave Technology, 42, 4233-4259(2024).

[17] Grieves M. Digital twin: manufacturing excellence through virtual factory replication[EB/OL]. https:∥www.3ds.com/fileadmin/PRODUCTS-SERVICES/DELMIA/PDF/Whitepaper/DELMIA-APRISO-Digital-Twin-Whitepaper.pdf

[18] Liu M N, Fang S L, Dong H Y et al. Review of digital twin about concepts, technologies, and industrial applications[J]. Journal of Manufacturing Systems, 58, 346-361(2021).

[19] Tao F, Zhang H, Liu A et al. Digital twin in industry: state-of-the-art[J]. IEEE Transactions on Industrial Informatics, 15, 2405-2415(2019).

[20] Ferrari A, Filer M, Balasubramanian K et al. GNPy: an open source application for physical layer aware open optical networks[J]. Journal of Optical Communications and Networking, 12, C31-C40(2020).

[21] Curri V. GNPy model of the physical layer for open and disaggregated optical networking[J]. Journal of Optical Communications and Networking, 14, C92-C104(2022).

[22] Barzegar S, Ruiz M, Sgambelluri A et al. Soft-failure detection, localization, identification, and severity prediction by estimating QoT model input parameters[J]. IEEE Transactions on Network and Service Management, 18, 2627-2640(2021).

[23] Sequeira D, Ruiz M, Costa N et al. OCATA: a deep-learning-based digital twin for the optical time domain[J]. Journal of Optical Communications and Networking, 15, 87-97(2023).

[24] Liu X M, Lun H Z, Gao R X et al. A data-fusion-assisted telemetry layer for autonomous optical networks[J]. Journal of Lightwave Technology, 39, 3400-3411(2021).

[28] Savory S J. Digital coherent optical receivers: algorithms and subsystems[J]. IEEE Journal of Selected Topics in Quantum Electronics, 16, 1164-1179(2010).

[29] Lal V, Studenkov P, Frost T et al. 1.6 Tbps coherent 2-channel transceiver using a monolithic Tx/Rx InP PIC and single SiGe ASIC[C], 1-3(2020).

[30] Roberts K, O’Sullivan M, Wu K T et al. Performance of dual-polarization QPSK for optical transport systems[J]. Journal of Lightwave Technology, 27, 3546-3559(2009).

[31] Torrengo E, Cigliutti R, Bosco G et al. Experimental validation of an analytical model for nonlinear propagation in uncompensated optical links[J]. Optics Express, 19, B790-B798(2011).

[32] Lavigne B, Bertran-Pardo O, Bresson C et al. 400 Gb/s real-time trials on commercial systems for next generation networks[J]. Journal of Lightwave Technology, 34, 477-483(2016).

[33] Laperle C, O’Sullivan M. Advances in high-speed DACs, ADCs, and DSP for optical coherent transceivers[J]. Journal of Lightwave Technology, 32, 629-643(2014).

[34] Kuschnerov M, Hauske F N, Piyawanno K et al. DSP for coherent single-carrier receivers[J]. Journal of Lightwave Technology, 27, 3614-3622(2009).

[35] Kaliteevskiy N A, Ivanov V, Sterlingov P et al. Transponder implementation penalty-accounted Gaussian-noise-based performance modeling of fiber-optic transmission systems[J]. Journal of Lightwave Technology, 38, 2253-2261(2020).

[36] Zhang L, Li X, Tang Y et al. A survey on QoT prediction using machine learning in optical networks[J]. Optical Fiber Technology, 68, 102804(2022).

[37] Ayassi R, Triki A, Crespi N et al. Survey on the use of machine learning for quality of transmission estimation in optical transport networks[J]. Journal of Lightwave Technology, 40, 5803-5815(2022).

[38] Kozdrowski S, Cichosz P, Paziewski P et al. Machine learning algorithms for prediction of the quality of transmission in optical networks[J]. Entropy, 23, 7(2021).

[39] Marcuse D, Manyuk C R, Wai P K A. Application of the Manakov-PMD equation to studies of signal propagation in optical fibers with randomly varying birefringence[J]. Journal of Lightwave Technology, 15, 1735-1746(1997).

[40] Deiterding R, Glowinski R, Oliver H et al. A reliable split-step Fourier method for the propagation equation of ultra-fast pulses in single-mode optical fibers[J]. Journal of Lightwave Technology, 31, 2008-2017(2013).

[41] Sinkin O V, Holzlohner R, Zweck J et al. Optimization of the split-step Fourier method in modeling optical-fiber communications systems[J]. Journal of Lightwave Technology, 21, 61-68(2003).

[42] Serena P, Lasagni C, Musetti S et al. On numerical simulations of ultra-wideband long-haul optical communication systems[J]. Journal of Lightwave Technology, 38, 1019-1031(2020).

[43] Chen X, Shieh W. Closed-form expressions for nonlinear transmission performance of densely spaced coherent optical OFDM systems[J]. Optics Express, 18, 19039-19054(2010).

[44] Carena A, Curri V, Bosco G et al. Modeling of the impact of nonlinear propagation effects in uncompensated optical coherent transmission links[J]. Journal of Lightwave Technology, 30, 1524-1539(2012).

[45] Poggiolini P, Bosco G, Carena A et al. The GN-model of fiber non-linear propagation and its applications[J]. Journal of Lightwave Technology, 32, 694-721(2014).

[46] Dar R, Feder M, Mecozzi A et al. Properties of nonlinear noise in long, dispersion-uncompensated fiber links[J]. Optics Express, 21, 25685-25699(2013).

[47] Carena A, Bosco G, Curri V et al. EGN model of non-linear fiber propagation[J]. Optics Express, 22, 16335-16362(2014).

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

[49] Semrau D, Saavedra G, Lavery D et al. A closed-form expression to evaluate nonlinear interference in Raman-amplified links[J]. Journal of Lightwave Technology, 35, 4316-4328(2017).

[50] Semrau D, Killey R I, Bayvel P. A closed-form approximation of the Gaussian noise model in the presence of inter-channel stimulated Raman scattering[J]. Journal of Lightwave Technology, 37, 1924-1936(2019).

[51] Poggiolini P, Jiang Y. Recent advances in the modeling of the impact of nonlinear fiber propagation effects on uncompensated coherent transmission systems[J]. Journal of Lightwave Technology, 35, 458-480(2017).

[52] Poggiolini P, Bosco G, Carena A et al. A simple and effective closed-form GN model correction formula accounting for signal non-Gaussian distribution[J]. Journal of Lightwave Technology, 33, 459-473(2015).

[53] Poggiolini P, Jiang Y, Carena A et al. Analytical modeling of the impact of fiber non-linear propagation on coherent systems and networks[M]. Enabling technologies for high spectral-efficiency coherent optical communication networks, 247-310(2016).

[54] Semrau D, Sillekens E, Killey R I et al. A modulation format correction formula for the Gaussian noise model in the presence of inter-channel stimulated Raman scattering[J]. Journal of Lightwave Technology, 37, 5122-5131(2019).

[55] Jiang Y, Nespola A, Tanzi A et al. Experimental test of closed-form EGN model over C+L bands[J]. IET Conference Proceedings, 254-257(2023).

[56] Yang J Q, Buglia H, Sillekens E et al. Experimental validation of the closed-form GN model accounting for distributed Raman amplification in an S+C+L-band hybrid amplified long-haul transmission system[C], 67-70(2024).

[57] Zhang F Y, MacKay A W. Closed-form coherent Gaussian noise model applicable to arbitrary flexible grid and heterogeneous links[C], 1-3(2024).

[58] Zhang Y H, Gan L, Qiu Q Z et al. Accurate closed-form coherent EGN model supporting distributed Raman amplification[C], 1-3(2024).

[59] Qiu Q Z, Lun H Z, Liu X M et al. Fourier neural operator based fibre channel modelling for optical transmission[C], 1-4(2022).

[60] Liu X M, Fan Y Y, Zhang Y H et al. Fusing physics to fiber nonlinearity model for optical networks based on physics-guided neural networks[J]. Journal of Lightwave Technology, 40, 5793-5802(2022).

[61] Ruan J Y, Lao H X, Lin M B et al. Extended L-band erbium-doped fiber amplifier with high gain using two-stage double-pass configuration[J]. Acta Optica Sinica, 43, 2206006(2023).

[62] Yu J K, Zhu S X, Gutterman C L et al. Machine-learning-based EDFA gain estimation[J]. Journal of Optical Communications and Networking, 13, B83-B91(2021).

[63] da Ros F, de Moura U C, Yankov M P. Machine learning-based EDFA gain model generalizable to multiple physical devices[C], 1-4(2020).

[64] Saleh A A M, Jopson R M, Evankow J D et al. Modeling of gain in erbium-doped fiber amplifiers[J]. IEEE Photonics Technology Letters, 2, 714-717(1990).

[65] Giles C R, Desurvire E. Modeling erbium-doped fiber amplifiers[J]. Journal of Lightwave Technology, 9, 271-283(1991).

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

[67] Liu X M, Zhang Y H, Chen Y L et al. Digital twin modeling and controlling of optical power evolution enabling autonomous-driving optical networks: a Bayesian approach[J]. Advanced Photonics, 6, 026006(2024).

[68] Liu Y C, Liu X M, Zhang Y H et al. Building a digital twin of an EDFA for optical networks: a gray-box modeling approach[J]. Journal of Optical Communications and Networking, 15, 830-838(2023).

[69] Bromage J. Raman amplification for fiber communications systems[J]. Journal of Lightwave Technology, 22, 79-93(2004).

[70] Dormand J R, Prince P J. A family of embedded Runge-Kutta formulae[J]. Journal of Computational and Applied Mathematics, 6, 19-26(1980).

[71] Zhou J H, Chen J P, Li X W et al. Robust, compact, and flexible neural model for a fiber Raman amplifier[J]. Journal of Lightwave Technology, 24, 2362-2367(2006).

[72] Ionescu M. Machine learning for ultrawide bandwidth amplifier configuration[C], 1-4(2019).

[73] Brusin A M R, Curri V, Zibar D et al. An ultra-fast method for gain and noise prediction of Raman amplifiers[C]. Ireland, 1-4(2019).

[74] Zibar D, Rosa Brusin A M, de Moura U C et al. Inverse system design using machine learning: the Raman amplifier case[J]. Journal of Lightwave Technology, 38, 736-753(2020).

[75] Rosa Brusin A M, de Moura U C, Curri V et al. Introducing load aware neural networks for accurate predictions of Raman amplifiers[J]. Journal of Lightwave Technology, 38, 6481-6491(2020).

[76] de Moura U C, Rosa Brusin A M, Carena A et al. Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning[J]. Optics Letters, 46, 1157-1160(2021).

[77] Marcon G, Galtarossa A, Palmieri L et al. Model-aware deep learning method for Raman amplification in few-mode fibers[J]. Journal of Lightwave Technology, 39, 1371-1380(2021).

[78] Zhang Y H, Liu X M, Gao R X et al. Raman pump optimization for maximizing capacity of C L optical transmission systems[J]. Journal of Lightwave Technology, 40, 7814-7825(2022).

[79] de Moura U C, Zibar D, Rosa Brusin A M et al. Fiber-agnostic machine learning-based Raman amplifier models[J]. Journal of Lightwave Technology, 41, 83-95(2023).

[80] Yankov M P, da Ros F, de Moura U C et al. Flexible Raman amplifier optimization based on machine learning-aided physical stimulated Raman scattering model[J]. Journal of Lightwave Technology, 41, 508-514(2023).

[81] Rosa Brusin A M, Nespola A, Zefreh M R et al. ML-based spectral power profiles prediction in presence of ISRS for ultra-wideband transmission[J]. Journal of Lightwave Technology, 42, 37-47(2024).

[82] Liu X M, Zhang Y H, Cai M et al. SMOF: simultaneous modeling and optimization framework for Raman amplifiers in C L-band optical networks[J]. Journal of Lightwave Technology, 42, 3174-3183(2024).

[83] Zhang Y H, Liu X M, Liu Y C et al. Physics-informed grey-box model for C+L band Raman amplification incorporating launch power profiles[J]. Optics Express, 33, 19094-19107(2025).

[84] Delezoide C, Ramantanis P, Layec P. On the performance prediction of optical transmission systems in presence of filtering[C], 1-4(2017).

[85] Delezoide C, Ramantanis P, Layec P. Weighted filter penalty prediction for QoT estimation[C], 1-3(2018).

[86] de Jauregui Ruiz I F, Ghazisaeidi A, Zami T et al. An accurate model for system performance analysis of optical fibre networks with in-line filtering[C]. Ireland, 1-4(2019).

[87] Zhang B, Zhang R, Zhang Q et al. Optical filtering penalty estimation using artificial neural network in elastic optical networks with cascaded reconfigurable optical add-drop multiplexers[J]. Optical Engineering, 58, 076105(2019).

[88] Sasai T. Digital longitudinal monitoring of optical transmission link[C], 1-4(2022).

[89] Sasai T, Takahashi M, Nakamura M et al. Linear least squares estimation of fiber-longitudinal optical power profile[J]. Journal of Lightwave Technology, 42, 1955-1965(2024).

[90] Sasai T, Nakamura M, Yamazaki E et al. Precise longitudinal power monitoring over 2080 km enabled by step size selection of split step Fourier method[C], 1-3(2022).

[91] Kashi A S, Zhuge Q B, Cartledge J C et al. Nonlinear signal-to-noise ratio estimation in coherent optical fiber transmission systems using artificial neural networks[J]. Journal of Lightwave Technology, 36, 5424-5431(2018).

[92] Liu X M, Lun H Z, Fu M F et al. Machine learning based fiber nonlinear noise monitoring for subcarrier-multiplexing systems[C], 1-3(2020).

[93] Cai M, Liu X M, Liu L et al. Optical filtering impairment monitoring based on model fusion for optical networks[J]. Optics Express, 30, 24639-24654(2022).

[94] Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 22, 1345-1359(2010).

[95] Zhuang F Z, Qi Z Y, Duan K Y et al. A comprehensive survey on transfer learning[J]. Proceedings of the IEEE, 109, 43-76(2021).

[96] Liu X M, Lun H Z, Fu M F et al. A three-stage training framework for customizing link models for optical networks[C], 1-3(2020).

[97] Ferrari A, Balasubramanian K, Filer M et al. Assessment on the in-field lightpath QoT computation including connector loss uncertainties[J]. Journal of Optical Communications and Networking, 13, A156-A164(2021).

[98] Karandin O, Ferrari A, Musumeci F et al. Probabilistic low-margin optical-network design with multiple physical-layer parameter uncertainties[J]. Journal of Optical Communications and Networking, 15, C129-C137(2023).

[99] Seve E, Pesic J, Delezoide C et al. Learning process for reducing uncertainties on network parameters and design margins[J]. Journal of Optical Communications and Networking, 10, A298-A306(2018).

[100] Seve E, Pesic J, Pointurier Y. Associating machine-learning and analytical models for quality of transmission estimation: combining the best of both worlds[J]. Journal of Optical Communications and Networking, 13, C21-C30(2021).

[101] He Y, Zhai Z Q, Dou L et al. Improved QoT estimations through refined signal power measurements and data-driven parameter optimizations in a disaggregated and partially loaded live production network[J]. Journal of Optical Communications and Networking, 15, 638-648(2023).

[102] Borraccini G, Straullu S, Piciaccia S et al. Cognitive Raman amplifier control using an evolutionary optimization strategy[J]. IEEE Photonics Technology Letters, 34, 223-226(2022).

[103] Song Y C, Zhang M, Zhang Y et al. Implementing digital twin in field-deployed optical networks: uncertain factors, operational guidance, and field-trial demonstration[J]. IEEE Network, 38, 38-45(2024).

[104] Borraccini G, Huang Y K, D’Amico A et al. Optical line physical parameters calibration in presence of EDFA total power monitors[C], 1-3(2024).

[105] Zhou J, Lu J N, Yu C Y. Improving the accuracy of QoT estimation with insertion loss distribution evaluation for C+L band transmission systems[J]. Journal of Optical Communications and Networking, 16, 12-20(2024).

[106] Ayassi R, Triki A, Laye M et al. Bayesian optimization-based algorithm to improve the quality of transmission estimation[C]. DC, NeF2B.3-29(2021).

[107] Ayassi R, Triki A, Le Guyader B et al. Field trial to assess Bayesian optimization for improving QoT estimation[C], 389-392(2023).

[108] Morette N, Hafermann H, Frignac Y et al. Machine learning enhancement of a digital twin for wavelength division multiplexing network performance prediction leveraging quality of transmission parameter refinement[J]. Journal of Optical Communications and Networking, 15, 333-343(2023).

[109] Zhang Y H, Liu X M, Qiu Q Z et al. Mapping-finding input-parameter refinement paradigm for a dynamic multiband optical network digital twin: the Raman amplifier modeling case[J]. Journal of Optical Communications and Networking, 16, 1059-1069(2024).

[110] Zhang Y H, Qiu Q Z, Liu X M et al. SAMA-IR: comprehensive input refinement methodology for optical networks with field-trial validation[C], 1-3(2025).