{"title":"Machine Learning Methods for Compensating Signal Distortions in Fiber-Optic Communication Lines","authors":"O. S. Sidelnikov, A. A. Redyuk, M. P. Fedoruk","doi":"10.3103/s8756699024700018","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The article addresses current issues in the field of fiber-optic data transmission, related to the constant increase in demand for communication system bandwidth and nonlinear response. The main machine learning methods used to compensate for nonlinear signal distortions in long-haul coherent communication lines are presented, including neural networks of various architectures. The paper emphasizes the promise of machine learning-based solutions for enhancing the performance of optical fiber communication systems, thanks to their ability to derive effective and adaptive signal recovery schemes with low computational complexity.</p>","PeriodicalId":44919,"journal":{"name":"Optoelectronics Instrumentation and Data Processing","volume":"13 1","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optoelectronics Instrumentation and Data Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3103/s8756699024700018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
The article addresses current issues in the field of fiber-optic data transmission, related to the constant increase in demand for communication system bandwidth and nonlinear response. The main machine learning methods used to compensate for nonlinear signal distortions in long-haul coherent communication lines are presented, including neural networks of various architectures. The paper emphasizes the promise of machine learning-based solutions for enhancing the performance of optical fiber communication systems, thanks to their ability to derive effective and adaptive signal recovery schemes with low computational complexity.
期刊介绍：
The scope of Optoelectronics, Instrumentation and Data Processing encompasses, but is not restricted to, the following areas: analysis and synthesis of signals and images; artificial intelligence methods; automated measurement systems; physicotechnical foundations of micro- and optoelectronics; optical information technologies; systems and components; modelling in physicotechnical research; laser physics applications; computer networks and data transmission systems. The journal publishes original papers, reviews, and short communications in order to provide the widest possible coverage of latest research and development in its chosen field.
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