[1] Sun M F, Cai Y C, Zhu M et al. Survey of field-signal recovery technique in direct-detection optical fiber communication system[J]. Laser & Optoelectronics Progress, 59, 1100002(2022).

[2] Ju J Z, Liu Q T, Li H Z et al. Multi-task optical performance monitoring based on convolutional neural network[J]. Acta Optica Sinica, 42, 2206002(2022).

[3] Fu L H. Investigation on optical performance monitoring based on optimized radial basis function artificial neural networks[J]. Laser & Optoelectronics Progress, 48, 080602(2011).

[4] Berthold J, Saleh A A M, Blair L et al. Optical networking: past, present, and future[J]. Journal of Lightwave Technology, 26, 1104-1118(2008).

[5] Shen T S R, Meng K, Lau A P T et al. Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms[J]. IEEE Photonics Technology Letters, 22, 1665-1667(2010).

[6] Khan F N, Fan Q R, Lu C et al. An optical communication's perspective on machine learning and its applications[J]. Journal of Lightwave Technology, 37, 493-516(2019).

[7] Saif W S, Esmail M A, Ragheb A M et al. Machine learning techniques for optical performance monitoring and modulation format identification: a survey[J]. IEEE Communications Surveys & Tutorials, 22, 2839-2882(2020).

[8] Tanimura T, Hoshida T, Rasmussen J C et al. OSNR monitoring by deep neural networks trained with asynchronously sampled data[C](2016).

[9] Cho H J, Lippiatt D, Varughese S et al. Convolutional neural networks for optical performance monitoring[C](2019).

[10] Wang D S, Wang M Y, Zhang M et al. Cost-effective and data size-adaptive OPM at intermediated node using convolutional neural network-based image processor[J]. Optics Express, 27, 9403-9419(2019).

[11] Luo H J, Huang Z L, Wu X et al. Cost-effective multi-parameter optical performance monitoring using multi-task deep learning with adaptive ADTP and AAH[J]. Journal of Lightwave Technology, 39, 1733-1741(2021).

[12] Tizikara D K, Serugunda J, Katumba A. Machine learning-aided optical performance monitoring techniques: a review[J]. Frontiers in Communications and Networks, 2, 756513(2022).

[13] Dong Z H, Khan F N, Sui Q et al. Optical performance monitoring: a review of current and future technologies[J]. Journal of Lightwave Technology, 34, 525-543(2016).

[14] Zhang H Y, Zhang D, Xue Y L. Constellation diagram analyzer based on few shot learning[C](2022).

[15] Du J H, Yang T, Chen X et al. A CNN-based cost-effective modulation format identification scheme by low-bandwidth direct detecting and low rate sampling for elastic optical networks[J]. Optics Communications, 471, 126007(2020).

[16] Liu X M, Lun H Z, Fu M F et al. AI-based modeling and monitoring techniques for future intelligent elastic optical networks[J]. Applied Sciences, 10, 363(2020).

[17] Xia L, Zhang J, Hu S H et al. Transfer learning assisted deep neural network for OSNR estimation[J]. Optics Express, 27, 19398-19406(2019).

[18] Lai J S, Yang A Y, Sun Y N. Optical fiber communication performance monitoring based on asynchronous delay tap sampling[J]. Acta Optica Sinica, 32, 1106004(2012).

[19] Fu J C, Chen S Q, Xue Y et al. Estimation of OSNR using ADTP and convolutional neural network under different chromatic dispersion[C], 1260-1268(2021).

[20] Fan X J, Xie Y L, Ren F et al. Joint optical performance monitoring and modulation format/bit-rate identification by CNN-based multi-task learning[J]. IEEE Photonics Journal, 10, 7906712(2018).

[21] Jargon J A, Wu X X, Willner A E. Optical performance monitoring by use of artificial neural networks trained with parameters derived from delay-tap asynchronous sampling[C], OThH1(2009).

[22] Sohn K, Berthelot D, Li C L et al. FixMatch: simplifying semi-supervised learning with consistency and confidence[EB/OL]. https://arxiv.org/abs/2001.07685

[23] Yu Z M, Wan Z Q, Shu L A et al. Loss weight adaptive multi-task learning based optical performance monitor for multiple parameters estimation[J]. Optics Express, 27, 37041-37055(2019).

[24] Streeter M J V, Colgan C, Cobo C C et al. Laser Wakefield accelerator modelling with variational neural networks[J]. High Power Laser Science and Engineering, 11, e9(2023).

[25] Tanimura T, Hoshida T, Kato T et al. Convolutional neural network-based optical performance monitoring for optical transport networks[J]. Journal of Optical Communications and Networking, 11, A52-A59(2018).

[26] Lin J P, Haberstroh F, Karsch S et al. Applications of object detection networks in high-power laser systems and experiments[J]. High Power Laser Science and Engineering, 11, e7(2023).

[27] Liu X X, Hao W H, Yang Z H et al. High-peak-power random Yb-fiber laser with intracavity Raman-frequency comb generation[J]. High Power Laser Science and Engineering, 11, e11(2023).

[28] Wang C X, Fu S N, Wu H et al. Joint OSNR and CD monitoring in digital coherent receiver using long short-term memory neural network[J]. Optics Express, 27, 6936-6945(2019).

[29] Tarvainen A, Valpola H. Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results[EB/OL]. https://arxiv.org/abs/1703.01780