Scalable methods for optical transmission performance prediction using machine learning (ML) are studied in metro reconfigurable optical add-drop multiplexer (ROADM) networks. A cascaded learning framework is introduced to encompass the use of cascaded component models for end-to-end (E2E) optical path prediction augmented with different combinations of E2E performance data and models. Additional E2E optical path data and models are used to reduce the prediction error accumulation in the cascade. Off-line training (pre-trained prior to deployment) and transfer learning are used for component-level erbium-doped fiber amplifier (EDFA) gain models to ensure scalability. Considering channel power prediction, we show that the data collection process of the pre-trained EDFA model can be reduced to only 5% of the original training set using transfer learning. We evaluate the proposed method under three different topologies with field deployed fibers and achieve a mean absolute error of 0.16 dB with a single (one-shot) E2E measurement on the deployed 6-span system with 12 EDFAs.
{"title":"Multi-span optical power spectrum prediction using cascaded learning with one-shot end-to-end measurement","authors":"Zehao Wang;Yue-Kai Huang;Shaobo Han;Daniel Kilper;Tingjun Chen","doi":"10.1364/JOCN.533634","DOIUrl":"https://doi.org/10.1364/JOCN.533634","url":null,"abstract":"Scalable methods for optical transmission performance prediction using machine learning (ML) are studied in metro reconfigurable optical add-drop multiplexer (ROADM) networks. A cascaded learning framework is introduced to encompass the use of cascaded component models for end-to-end (E2E) optical path prediction augmented with different combinations of E2E performance data and models. Additional E2E optical path data and models are used to reduce the prediction error accumulation in the cascade. Off-line training (pre-trained prior to deployment) and transfer learning are used for component-level erbium-doped fiber amplifier (EDFA) gain models to ensure scalability. Considering channel power prediction, we show that the data collection process of the pre-trained EDFA model can be reduced to only 5% of the original training set using transfer learning. We evaluate the proposed method under three different topologies with field deployed fibers and achieve a mean absolute error of 0.16 dB with a single (one-shot) E2E measurement on the deployed 6-span system with 12 EDFAs.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"17 1","pages":"A23-A33"},"PeriodicalIF":4.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Farhad Arpanaei;Mahdi Ranjbar Zefreh;Carlos Natalino;Piotr Lechowicz;Shuangyi Yan;Jose M. Rivas-Moscoso;Oscar Gonzalez de Dios;Juan Pedro Fernandez-Palacios;Hami Rabbani;Maite Brandt-Pearce;Alfonso Sanchez-Macian;Jose Alberto Hernandez;David Larrabeiti;Paolo Monti
Both multi-band and space division multiplexing (SDM) independently represent cost-effective approaches for next-generation optical backbone networks, particularly as data exchange between core data centers reaches the petabit-per-second scale. This paper focuses on different strategies for implementing band and SDM elastic optical network (BSDM EON) technology and analyzes the total network capacity of three sizes of backbone metro-core networks: ultra-long-, long-, and medium-distance networks related to the United States, Japan, and Spain, respectively. Two BSDM strategies are considered, namely, multi-core fibers (MCFs) and BSDM based on standard single-mode fiber (SSMF) bundles of multi-fiber pairs (BuMFPs). For MCF-based BSDM, we evaluated the performance of four manufactured trench-assisted weakly coupled (TAWC) MCFs with 4, 7, 13, and 19 cores. Simulation results reveal that, in the regime of ultra-low (UL) loss and inter-core crosstalk (ICXT), MCF-based throughput can be up to 14% higher than SSMF BuMFP-based BSDM when the core pitch exceeds 43 µm and the loss coefficient is lower than that of standard single-mode fibers. However, increasing the number of cores with (non-)standard cladding diameters, UL loss, and ICXT coefficient is not beneficial. As core counts increase up to 13 for non-standard cladding diameters ( ${lt}230;{unicode{x00B5}{rm m}}$