基于人工智能的光纤非线性均衡算法研究概述

李亚杰; 赵永利; 刘守东; 张杰

doi:10.11959/j.issn.1000-0801.2020054

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基于人工智能的光纤非线性均衡算法研究概述

研究与开发 | 更新时间：2024-06-05

- 基于人工智能的光纤非线性均衡算法研究概述
- Overview of research on fiber nonlinear equalization algorithm based on artificial intelligence
- 电信科学 2020年36卷第3期页码：61-70
- 作者机构：
- 作者简介：
  
  [ "李亚杰（1990- ），男，博士，北京邮电大学师资博士后，主要研究方向为光网络智能控制和安全光通信" ]
  [ "赵永利（1981- ），男，北京邮电大学教授、博士生导师，主要研究方向为人工智能光网络和安全光通信" ]
  [ "刘守东（1995- ），男，北京邮电大学硕士生，主要研究方向为人工智能与安全光传输" ]
  [ "张杰（1972- ），男，博士，北京邮电大学教授、博士生导师，光电信息学院院长，信息光子学与光通信国家重点实验室副主任，主要研究方向为安全光通信等" ]
- 基金信息：
  
  国家电网有限公司科技项目;The Technology Project of State Grid Corporation of China(5100-201940006A-0-0-00)
- DOI：10.11959/j.issn.1000-0801.2020054
  中图分类号： TN911.22
- 网络出版日期：2020-03，
  
  纸质出版日期：2020-03-20
- 稿件说明：
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李亚杰, 赵永利, 刘守东, 等. 基于人工智能的光纤非线性均衡算法研究概述[J]. 电信科学, 2020,36(3):61-70.

Yajie LI, Yongli ZHAO, Shoudong LIU, et al. Overview of research on fiber nonlinear equalization algorithm based on artificial intelligence[J]. Telecommunications science, 2020, 36(3): 61-70.
李亚杰, 赵永利, 刘守东, 等. 基于人工智能的光纤非线性均衡算法研究概述[J]. 电信科学, 2020,36(3):61-70. DOI： 10.11959/j.issn.1000-0801.2020054.

Yajie LI, Yongli ZHAO, Shoudong LIU, et al. Overview of research on fiber nonlinear equalization algorithm based on artificial intelligence[J]. Telecommunications science, 2020, 36(3): 61-70. DOI： 10.11959/j.issn.1000-0801.2020054.

摘要

概述了非线性均衡算法在光传输系统中的必要性与重要性，阐述了经典的非线性均衡算法原理，指出了经典算法的缺点与局限性。结合近几年的研究现状，详细介绍了 4 种基于人工智能的非线性均衡算法，包括人工神经网络、支持向量机、无监督聚类和深度神经网络，并从性能、复杂度、实时性、应用灵活性等方面进行了对比，最后展望分析了基于人工智能的非线性均衡未来的发展趋势。

Abstract

The necessity and importance of nonlinear equalization algorithm in optical transmission systems were outlined.The principle of classical nonlinear equalization algorithm was described

and the shortcomings and limitations of classical algorithms were presented.Combined with the status in recent years

four kinds of nonlinear equalization algorithms based on artificial intelligence were introduced in detail.These algorithms include artificial neural networks

support vector machine

unsupervised clustering and deep neural network.All the nonlinear equalization algorithms mentioned were compared in terms of performance

complexity

instantaneity and application flexibility.Finally

the future development trend of nonlinear equalization algorithm based on artificial intelligence was analyzed.

关键词

Keywords

references

吴金达 , 卢瑾 . 基于广义回归神经网络的CO-OFDM系统非线性均衡 [J ] . 光学学报 , 2018 , 38 ( 9 ): 78 - 86 .

WU J D , LU J . Nonlinear equalizer based on general regression neural network in coherent optical OFDM system [J ] . Acta Optica Sinica , 2018 , 38 ( 9 ): 78 - 86 .

LU C H , FENG K M . Reduction of high PAPR effect with FEC enhanced deep data clipping ratio in an optical OFDM system [C ] // Proceedings of Conference on Lasers and Electro-Opticals Society . Piscataway:IEEE Press , 2007 : 941 - 942 .

BULAKCI O , SCHUSTER M , BUNGE C A , et al . Precoding based peak-to-average power ratio reduction for optical OFDM demonstrated on compatible single sideband modulation with direct detection [C ] // Proceedings of 2008 National Fiber Optic Engineers Conference . Piscataway:IEEE Press , 2008 : 1 - 3 .

KRONGOLD B S , TANG Y , SHIEH W . Fiber non-linearity mitigation by PAPR reduction in coherent optical OFDM systems via active constellation extension [C ] // Proceedings of 2008 34th European Conference on Optical Communication . Piscataway:IEEE Press , 2008 : 1 - 2 .

GIACOUMIDIS E . Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM [J ] . Future Internet , 2019 , 11 ( 1 ): 1 - 20 .

GIACOUMIDIS E . Blind non-linearity equalization by machine-learning-based clustering for single-and multichannel coherent optical OFDM [J ] . Journal of Lightwave Technology , 2018 , 36 ( 6 ): 721 - 727 .

IP E M , KAHN J M . Fiber impairment compensation using coherent detection and digital signal processing [J ] . Journal of Lightwave Technology , 2010 , 28 ( 4 ): 502 - 519 .

GAO G J , ZHANG J , GU W Y . Analytical evaluation of practical DBP-based intra-channel nonlinearity compensators [J ] . IEEE Photonics Technology Letters , 2013 , 25 ( 8 ): 717 - 720 .

CHUN-YEN C , . Sparse volterra nonlinear equalizer by employing pruning algorithm for high-speed PAM-4 850-nm VCSEL optical interconnect [C ] // Proceedings of Optical Fiber Communication Conference . Piscataway:IEEE Press , 2019 .

JARAJREH M A . Artificial neural network nonlinear equalizer for coherent optical OFDM [J ] . IEEE Photonics Technology Letters , 2015 , 27 ( 4 ): 387 - 390 .

GIACOUMIDIS E , . Intra and inter-channel nonlinearity compensation in WDM coherent optical OFDM using artificial neural network based nonlinear equalization [C ] // Proceedings of Process Optical Fiber Communication Conference . Piscataway:IEEE Press , 2019 .

AHMED G R . Nonlinear equalizer based on neural networks for PAM-4 signal transmission using DML [J ] . IEEE Photonics Technology Letters , 2018 , 30 ( 15 ): 1416 - 1419 .

VAQUERO F J , . Joint estimation of linear and non-linear signal-to-noise ratio based on neural networks [C ] // Proceedings of Optical Fiber Communication Conference . Piscataway:IEEE Press , 2019 .

WANG D S , . Nonlinear decision boundary created by a machine learning-based classifier to mitigate nonlinear phase noise [C ] // Proceedings of European Conference on Optical Communications.[S.l.:s.n] . 2015 .

NGUYEN T , MHATLI S , GIACOUMIDIS E , et al . Fiber nonlinearity equalizer based on support vector classification for coherent optical OFDM [J ] . IEEE Photonics Journal , 2016 , 8 ( 2 ): 711 - 721 .

GIACOUMIDIS E . Reduction of nonlinear inter-subcarrier intermixing in coherent optical OFDM by a fast newton-based support vector machine nonlinear equalizer [J ] . Journal of Lightwave Technology , 2017 , 35 ( 12 ): 2391 - 2397 .

GIACOUMIDIS E . Unsupervised support vector machines for nonlinear blind equalization in CO-OFDM [J ] . IEEE Photonics Technology Letters , 2018 , 30 ( 12 ): 1091 - 1094 .

ZHANG K , FAN Y Y , et al . Fiber nonlinear noise-to-signal ratio estimation by machine learning [C ] // Proceedings of Optical Fiber Communication Conference.[S.l.:s.n] . 2019 .

ZHANG J F . K-means-clustering-based fiber nonlinearity equalization techniques for 64-QAM coherent optical communication system [J ] . Optics Express , 2017 , 25 ( 22 ): 27570 - 27580 .

LU X Y , . Non-linear compensation of multi-CAP VLC system employing pre-distortion base on clustering of machine learning [C ] // Proceedings of Optical Fiber Communication Conference.[S.l.:s.n] . 2018 .

CHUANG C Y , . Sparse volterra nonlinear equalizer by employing pruning algorithm for high-speed PAM-4 850-nm VCSEL optical interconnect [C ] // Proceedings of Optical Fiber Communication Conference.[S.l.:s.n] . 2019 .

GIACOUMIDIS E , . Fiber nonlinear compensation using machine learning clustering [C ] // Proceedings of International Conference on Recent Innovations in Engineering and Technology.[S.l.:s.n] . 2019 .

GIACOUMIDIS E , . Real-time machine learning based fiber-induced nonlinearity compensation in energy-efficient coherent optical networks [C ] // Proceedings of European Conference on Optical Communications.[S.l.:s.n] . 2019 .

GIACOUMIDIS E . A blind nonlinearity compensator using DBSCAN clustering for coherent optical transmission systems [J ] . Applied Sciences , 2019 , 9 ( 20 ): 4398 - 4406 .

GAO Y L , ZIAD A . Reduced complexity nonlinearity compensation via principal component analysis and deep neural networks [C ] // Proceedings of Optical Fiber Communication Conference.[S.l.:s.n] . 2019 .

CHUANG C Y , . Convolutional neural network based nonlinear classifier for 112-Gbps high speed optical link [C ] // Proceedings of Optical Fiber Communication Conference.[S.l.:s.n] . 2018 .

LI P X , . 56 Gbit/s IM/DD PON based on 10G-class optical devices with 29 dB loss budget enabled by machine learning [C ] // Proceedings of Optical Fiber Communication Conference.[S.l.:s.n] . 2018 .

SCHAEDLER M , . Subcarrier power loading for coherent optical OFDM optimized by machine learning [C ] // Proceedings of Optical Fiber Communication Conference.[S.l.:s.n] . 2018 .

ZHANG S L . Field and lab experimental demonstration of nonlinear impairment conpensation using neural networks [J ] . Nature Communications , 2019 ( 10 ):3033.

KOIKE-AKINO T , . Neural turbo equalization to mitigate fiber nonlinearity [C ] // Proceedings of European Conference on Optical Communications.[S.l.:s.n . ] , 2019 .

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