Machine Learning Methods for Compensating Signal Distortions in Fiber-Optic Communication Lines

IF 0.5 Q4 PHYSICS, MULTIDISCIPLINARY

Optoelectronics Instrumentation and Data Processing Pub Date : 2024-06-20 DOI:10.3103/s8756699024700018

O. S. Sidelnikov, A. A. Redyuk, M. P. Fedoruk

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.

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补偿光纤通信线路信号失真的机器学习方法

摘要文章论述了光纤数据传输领域当前存在的问题，这些问题与对通信系统带宽和非线性响应需求的不断增长有关。文章介绍了用于补偿长途相干通信线路中非线性信号失真的主要机器学习方法，包括各种架构的神经网络。论文强调了基于机器学习的解决方案在提高光纤通信系统性能方面的前景，因为这些方案能够以较低的计算复杂度推导出有效的自适应信号恢复方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Optoelectronics Instrumentation and Data Processing PHYSICS, MULTIDISCIPLINARY-

CiteScore

1.00

自引率

50.00%

发文量

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.