{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T13:43:06Z","timestamp":1761745386028,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T00:00:00Z","timestamp":1609977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62001181 and 61471179"],"award-info":[{"award-number":["62001181 and 61471179"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the China Postdoctoral Science Foundation funded project","award":["2018M642843"],"award-info":[{"award-number":["2018M642843"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, asynchronous complex histogram (ACH)-based multi-task artificial neural networks (MT-ANNs), are proposed to realize modulation format identification (MFI), optical signal-to-noise ratio (OSNR) estimation and fiber nonlinear (NL) noise power estimation simultaneously for coherent optical communication. Optical performance monitoring (OPM) is demonstrated with polarization mode multiplexing (PDM), 16 quadrature amplitude modulation (QAM), PDM-32QAM, as well as PDM-star 16QAM (S-16QAM) for the first time. The range of launched power is \u22123 to \u22122 dBm with a fiber link of 160\u20131600 km. Then, the accuracy of MFI reaches 100%. The average root mean square error (RMSE) of OSNR estimation can reach 0.37 dB. The average RMSE of NL noise power estimation can reach 0.25 dB. The results show that the monitoring scheme is robust to the increase of fiber length, and the solution can monitor more optical network parameters with better performance and fewer training data, simultaneously. The proposed ACH MT-ANN has certain reference significance for the future long-haul coherent OPM system.<\/jats:p>","DOI":"10.3390\/s21020380","type":"journal-article","created":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T23:03:42Z","timestamp":1610319822000},"page":"380","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Joint Fiber Nonlinear Noise Estimation, OSNR Estimation and Modulation Format Identification Based on Asynchronous Complex Histograms and Deep Learning for Digital Coherent Receivers"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8479-4458","authenticated-orcid":false,"given":"Shuailong","family":"Yang","sequence":"first","affiliation":[{"name":"The School of Optics and Electronics Information, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6588-9902","authenticated-orcid":false,"given":"Liu","family":"Yang","sequence":"additional","affiliation":[{"name":"The School of Optics and Electronics Information, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Fengguang","family":"Luo","sequence":"additional","affiliation":[{"name":"The School of Optics and Electronics Information, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Bin","family":"Li","sequence":"additional","affiliation":[{"name":"The School of Optics and Electronics Information, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Xiaobo","family":"Wang","sequence":"additional","affiliation":[{"name":"The School of Optics and Electronics Information, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Yuting","family":"Du","sequence":"additional","affiliation":[{"name":"The School of Optics and Electronics Information, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Deming","family":"Liu","sequence":"additional","affiliation":[{"name":"The School of Optics and Electronics Information, Huazhong University of Science and Technology, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"C12","DOI":"10.1364\/JOCN.9.000C12","article-title":"Beyond 100 Gb\/s: Capacity, Flexibility, and Network Optimization","volume":"9","author":"Roberts","year":"2017","journal-title":"J. Opt. Commun. Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.optcom.2015.03.041","article-title":"Accumulation of nonlinear noise in coherent communication lines without dispersion compensation","volume":"34","author":"Konyshev","year":"2015","journal-title":"Opt. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"D42","DOI":"10.1364\/JOCN.10.000D42","article-title":"Machine Learning based Linear and Nonlinear Noise Estimation","volume":"10","author":"Lves","year":"2018","journal-title":"J. Opt. Commun. Netw."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1109\/JPHOT.2011.2112342","article-title":"Information Spectral Efficiency and Launch Power Density Limits Due to Fiber Nonlinearity for Coherent Optical OFDM Systems","volume":"3","author":"Shieh","year":"2011","journal-title":"IEEE Photonics J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"25685","DOI":"10.1364\/OE.21.025685","article-title":"Properties of nonlinear noise in long, dispersion-uncompensated fiber links","volume":"21","author":"Dar","year":"2013","journal-title":"Opt. Express"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dou, L., Yamauchi, T., Su, X., Tao, Z., Oda, S., Aoki, Y., Hoshida, T., and Rasmussen, J.C. (2016, January 20\u201324). An accurate nonlinear noise insensitive OSNR monitor. Proceedings of the Optical Fiber Communications Conference and Exposition, Anaheim, CA, USA.","DOI":"10.1364\/OFC.2016.W3A.5"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"19520","DOI":"10.1364\/OE.20.019520","article-title":"OSNR monitoring for QPSK and 16-QAM systems in presence of fiber nonlinearities for digital coherent receivers","volume":"20","author":"Dong","year":"2012","journal-title":"Opt. Express"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Khan, F.N., Lu, C., and Lau, A.P.T. (2018, January 11\u201315). Optical performance monitoring in fiber-optic networks enabled by machine learning techniques. Proceedings of the Optical Fiber Communications Conference and Exposition, San Diego, CA, USA.","DOI":"10.1364\/OFC.2018.M2F.3"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1109\/COMST.2018.2880039","article-title":"An overview on application of machine learning techniques in optical networks","volume":"21","author":"Musumeci","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1109\/JLT.2016.2590989","article-title":"Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals","volume":"35","author":"Thrane","year":"2017","journal-title":"J. Lightwave Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"19398","DOI":"10.1364\/OE.27.019398","article-title":"Transfer learning assisted deep neural network for OSNR estimation","volume":"27","author":"Xia","year":"2019","journal-title":"Opt. Express"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"A52","DOI":"10.1364\/JOCN.11.000A52","article-title":"Convolutional Neural Network-Based Optical Performance Monitoring for Optical Transport Networks","volume":"11","author":"Tanimura","year":"2019","journal-title":"J. Opt. Commun. Netw."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"17767","DOI":"10.1364\/OE.25.017767","article-title":"Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks","volume":"25","author":"Khan","year":"2017","journal-title":"Opt. Express"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2230","DOI":"10.1109\/JLT.2017.2772851","article-title":"Experimental Demonstration of Simultaneous Modulation Format\/Symbol Rate Identification and Optical Performance Monitoring for Coherent Optical Systems","volume":"36","author":"Guesmi","year":"2018","journal-title":"J. Lightwave Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"21346","DOI":"10.1364\/OE.26.021346","article-title":"OSNR and nonlinear noise power estimation for optical fiber communication systems using LSTM based deep learning technique","volume":"26","author":"Wang","year":"2018","journal-title":"Opt. Express"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tax, N., Verenich, I., Rosa, M.L., and Dumas, M. (2017). Predictive business process monitoring with LSTM neural networks. Adavanced Information Systems Engineering.","DOI":"10.1007\/978-3-319-59536-8_30"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, X., Lun, H., Fu, M., Yi, L., Hu, W., and Zhuge, Q. (2020, January 8\u201312). Machine Learning Based Fiber Nonlinear Noise Monitoring for Subcarrier-multiplexing Systems. Proceedings of the Optical Fiber Communication Conference, San Diego, CA, USA.","DOI":"10.1364\/OFC.2020.M2J.6"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3055","DOI":"10.1109\/JLT.2019.2910143","article-title":"Application of Machine Learning in Fiber Nonlinearity Modeling and Monitoring for Elastic Optical Networks","volume":"37","author":"Zhuge","year":"2019","journal-title":"J. Lightwave Technol."},{"key":"ref_19","first-page":"1","article-title":"Joint Optical Performance Monitoring and Modulation Format\/Bit-Rate Identification by CNN-Based Multi-Task Learning","volume":"10","author":"Fan","year":"2018","journal-title":"IEEE Photonics J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17150","DOI":"10.1364\/OE.25.017150","article-title":"Intelligent constellation diagram analyzer using convolutional neural network-based deep learning","volume":"25","author":"Wang","year":"2017","journal-title":"Opt. Express"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11281","DOI":"10.1364\/OE.27.011281","article-title":"Intelligent optical performance monitor using multi-task learning based artificial neural network","volume":"27","author":"Wan","year":"2019","journal-title":"Opt. Express"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4471","DOI":"10.1364\/OE.27.004471","article-title":"Modulation format identification and OSNR monitoring using density distributions in Stokes axes for digital coherent receivers","volume":"27","author":"Yi","year":"2019","journal-title":"Opt. Express"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xu, M., Zhang, J., Zhang, H., Jia, Z., Wang, J., Cheng, L., Campos, L.A., and Knittle, C. (2019, January 3\u20137). Multi-stage machine learning enhanced DSP for DP-64QAM coherent optical transmission systems. Proceedings of the Optical Fiber Communication Conference, San Diego, CA, USA.","DOI":"10.1364\/OFC.2019.M2H.1"},{"key":"ref_24","first-page":"1","article-title":"A Joint OSNR and Nonlinear Distortions Estimation Method for Optical Fiber Transmission System","volume":"10","author":"Xiang","year":"2018","journal-title":"IEEE Photonics J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1109\/LPT.2019.2910288","article-title":"Low-Complexity and Nonlinearity-Tolerant Modulation Format Identification Using Random Forest","volume":"31","author":"Zhao","year":"2019","journal-title":"IEEE Photonics Technol. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1109\/LPT.2017.2754501","article-title":"Modulation Classification Using Received Signal\u2019s Amplitude Distribution for Coherent Receivers","volume":"29","author":"Lin","year":"2017","journal-title":"IEEE Photonics Technol. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3815","DOI":"10.1109\/JLT.2019.2921305","article-title":"A Non-Data-Aided OSNR Estimation Algorithm for Coherent Optical Fiber Communication Systems Employing Multilevel Constellations","volume":"37","author":"Lin","year":"2019","journal-title":"J. Lightwave Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2127","DOI":"10.1109\/LPT.2018.2878530","article-title":"Joint Modulation Classification and OSNR Estimation Enabled by Support Vector Machine","volume":"30","author":"Lin","year":"2018","journal-title":"IEEE Photonics Technol. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7607","DOI":"10.1364\/OE.388491","article-title":"Transfer learning simplified multi-task deep neural network for PDM-64QAM optical performance monitoring","volume":"28","author":"Cheng","year":"2020","journal-title":"Opt. Express"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"19062","DOI":"10.1364\/OE.27.019062","article-title":"Multi-task deep neural network (MT-DNN) enabled optical performance monitoring from directly detected PDM-QAM signals","volume":"27","author":"Cheng","year":"2019","journal-title":"Opt. Express"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Khan, F.N., Fan, Q., Lu, J., Zhou, G., Lu, C., and Lau, A.P.T. (2020, January 8\u201312). Applications of Machine Learning in Optical Communications and Networks. Proceedings of the Optical Fiber Communication Conference, San Diego, CA, USA.","DOI":"10.1364\/OFC.2020.M1G.5"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8201","DOI":"10.1364\/OE.26.008201","article-title":"Doubly differential star-16-QAM for fast wavelength switching coherent optical packet transceiver","volume":"26","author":"Liu","year":"2018","journal-title":"Opt. Express"},{"key":"ref_33","unstructured":"Wu, J., Droppo, J., Deng, L., and Acero, A. (December, January 30). A noise-robust ASR front-end using wiener filter constructed from MMSE estimation of clean speech and noise. Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding, St. Thomas, VI, USA."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"16335","DOI":"10.1364\/OE.22.016335","article-title":"EGN model of non-linear fiber propagation","volume":"22","author":"Carena","year":"2014","journal-title":"Opt. Express"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/380\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:08:12Z","timestamp":1760159292000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/380"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,7]]},"references-count":34,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["s21020380"],"URL":"https:\/\/doi.org\/10.3390\/s21020380","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,1,7]]}}}