Application of Deep Learning in Impairments Compensation of Optical Fiber Communication

GUO Hong; DENG Pengcheng; YANG Hui

doi:10.13756/j.gtxyj.2024.220075

您当前的位置：

首页 >

文章列表页 >

Application of Deep Learning in Impairments Compensation of Optical Fiber Communication

更新时间：2024-04-02

- Application of Deep Learning in Impairments Compensation of Optical Fiber Communication
- Study on Optical Communications Issue 2, Pages: 22007501-22007512(2024)
- 作者机构：
  
  西南交通大学　信息科学与技术学院，成都　610031
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2024.220075
  CLC： TN929
- Published：10 April 2024，
  
  Received：22 January 2023，
  
  Revised：14 February 2023，
扫描看全文
郭虹，邓鹏程，杨慧. 深度学习在光纤通信系统损伤补偿中的应用[J].光通信研究，2024(2):220075.

Guo H, Deng P C, Yang H. Application of Deep Learning in Impairments Compensation of Optical Fiber Communication [J]. Study on Optical Communications, 2024(2):220075.
郭虹，邓鹏程，杨慧. 深度学习在光纤通信系统损伤补偿中的应用[J].光通信研究，2024(2):220075. DOI： 10.13756/j.gtxyj.2024.220075.

Guo H, Deng P C, Yang H. Application of Deep Learning in Impairments Compensation of Optical Fiber Communication [J]. Study on Optical Communications, 2024(2):220075. DOI： 10.13756/j.gtxyj.2024.220075.

摘要

随着社会信息化程度的逐步提升，人们对光纤通信系统容量的需求持续增长。信道损伤一直是限制光纤通信容量提升的关键因素，信道损伤补偿作为光纤通信系统的基础性功能和关键技术也在不断改进。近年来，将深度学习应用到光纤通信信道损伤补偿中更是吸引了不少研究人员的关注。文章介绍了近年来应用于光纤通信中的常见深度学习模型，阐述了深度学习技术在光纤通信系统信号损伤补偿方面的发展情况。

Abstract

With the development of the social informatization

the demand for optical fiber communication system capacity continues to grow. Channel impairment has always been the key factor limiting the capacity in optical fiber communication system. As the basic function and key technology in optical fiber communication system

channel impairment compensation is constantly improved. Applying deep learning technology in channel impairment compensation of optical fiber communication has attracted the attention of many researchers. This paper introduces the common deep learning models used and the development of deep learning technology for impairments compensation of optical fiber communication systems in recent years.

关键词

光纤通信系统深度学习神经网络损伤补偿

Keywords

fiber-optic communication systemdeep learningneural networkimpairments compensation

references

Watts P M, Mikhailov V, Savory S, et al. Performance of Single-mode Fiber Links Using Electronic Feed-forward and Decision Feedback Equalizers[J]. IEEE Photonics Technology Letters, 2005, 17(10): 2206-2208.

Agazzi O E, Hueda M R, Carrer H S, et al. Maximum-likelihood Sequence Estimation in Dispersive Optical Channels[J]. Journal of Lightwave Technology, 2005, 23(2): 749-763.

Alfiad M S, van den Borne D, Kuschnerov M, et al. FFE, DFE and MLSE Equalizers in Phase Modulated Transmission Systems[C]//2009 IEEE LEOS Annual Meeting Conference Proceedings. Belek-Antalya, Turkey: IEEE, 2009: 5343310.

Guiomar F P, Pinto A N. Simplified Volterra Series Nonlinear Equalizer for Polarization-multiplexed Coherent Optical Systems[J]. Journal of Lightwave Technology, 2013, 31(23): 3879-3891.

Ip E, Kahn J M.Compensation of Dispersion and Nonlinear Impairments Using Digital Backpropagation[J]. Journal of Lightwave Technology, 2008, 26(20), 3416-3425.

徐梦然, 崔晟, 李俊. 光纤通信系统中非线性损伤补偿技术的研究[J].光通信研究, 2013(5): 12-14.

Xu M R, Cui S, Li J. Advances of Research on Nonlinear Distortion Compensation Technologies for Fiber-optic Communication Systems[J].Study on Optical Communications, 2013(5): 12-14.

Dong S, Wang P, Abbas K. A Survey on Deep Learning and Its Applications[J]. Computer Science Review, 2021, 40: 100379.

孙志军, 薛磊, 许阳明, 等. 深度学习研究综述[J]. 计算机应用研究, 2012, 29(8): 2806-2810.

Sun Z J, Xue L, Xu Y M, et al. Overview of Deep Learning [J]. Application Research of Computers, 2012, 29(8): 2806-2810.

Hinton G E, Salakhutdinov R R. Reducing the Dimensionality of Data with Neural Networks[J]. Science, 2006, 313(5786): 504-507.

Khan F N, Fan Q, Lu C, et al. An Optical Communication's Perspective on Machine Learning and Its Applications[J]. Journal of Lightwave Technology, 2019, 37(2): 493-516.

Saif W S, Esmail M A, Ragheb A M, et al. Machine Learning Techniques for Optical Performance Monitoring and Modulation Format Identification: A Survey[J]. IEEE Communications Surveys & Tutorials, 2020, 22(4): 2839-2882.

Zhang S, Yaman F, Nakamura K, et al. Field and Lab Experimental Demonstration of Nonlinear Impairment Compensation Using Neural Networks[J]. Nature Communications, 2019, 10: 3033.

Sengupta S, Basak S, Saikia P, et al. A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends [J]. Knowledge-based Systems, 2020, 194: 105596.

Karanov B, Chagnon M, Thouin F, et al. End-to-end Deep Learning of Optical Fiber Communications[J]. Journal of Lightwave Technology, 2018, 36(20): 4843-4855.

Hubel D H, Wiesel T N. Receptive Fields of Cells in Striate Cortex of very Young, Visually Inexperienced Kittens[J]. Journal of Neurophysiology, 1963, 26: 994-1002.

LeCun Y, Bottou L, Bengio Y, et al. Gradient-based Learning Applied to Document Recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.

Gu J, Wang Z, Kuen J, et al. Recent Advances in Convolutional Neural Networks[J]. Pattern Recognition, 2018, 77: 354-377.

Boureau Y L, Ponce J, LeCun Y. A Theoretical Analysis of Feature Pooling in Visual Recognition[C]//Proceedings of the 27th International Conference on International Conference on Machine Learning. Haifa, Israel: ACM, 2010: 111-118.

Niu X X, Suen C Y. A Novel Hybrid CNN-SVM Classifier for Recognizing Handwritten Digits[J]. Pattern Recognition, 2012, 45(4): 1318-1325.

Russakovsky O, Deng J, Su H, et al. ImageNet Large Scale Visual Recognition Challenge[J]. International Journal of Computer Vision, 2015, 115(3): 211-252.

Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-scale Image Recognition[DB/OL]. (2015-04-10)[2022-11-22].https://arxiv.org/abs/1409.1556https://arxiv.org/abs/1409.1556.

Szegedy C, Liu W, Jia Y, et al. Going Deeper with Convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA: IEEE, 2015: 7298594.

LeCun Y, Bengio Y, Hinton G. Deep Learning[J]. Nature, 2015, 521(7553): 436-444.

Yu Y, Si X, Hu C, et al. A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures[J]. Neural Computation, 2019, 31(7): 1235-1270.

Goodfellow I J, Shlens J, Szegedy C. Explaining and Harnessing Adversarial Examples[DB/OL]. (2015-03-20)[2022-11-22]. https://arxiv.org/abs/1412.6572v1https://arxiv.org/abs/1412.6572v1.

Creswell A, White T, Dumoulin V, et al. Generative Adversarial Networks: an Overview[J]. IEEE Signal Processing Magazine, 2018, 35(1): 53-65.

Wang D, Zhang M. Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning[J]. Frontiers in Communications and Networks, 2021, 2: 656786.

Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative Adversarial Networks[J]. Communications of the ACM, 2020, 63(11): 139-144.

Nowozin S, Cseke B, Tomioka R. F-GAN: Training Generative Neural Samplers Using Variational Divergence Minimization[C]//30th Conference on Neural Information Processing Systems. Barcelona, Spain: ACM, 2016: 271-279.

Nagarajan V, Kolter J Z. Gradient Descent GAN Optimization is Locally Stable[DB/OL]. (2018-01-13)[2022-11-22]. https://arxiv.org/abs/1706.04156https://arxiv.org/abs/1706.04156.

Arjovsky M, Chintala S, Bottou L. Wasserstein Generative Adversarial Networks[C]// 34th International Conference on Machine Learning. Sydney, NSW, Australia: ACM, 2017: 214-223.

Rosca M, Lakshminarayanan B, Warde-Farley D, et al. Variational Approaches for Auto-encoding Generative Adversarial Networks[DB/OL]. (2017-10-21)[2022-11-22]. https://arxiv.org/abs/1706.04987https://arxiv.org/abs/1706.04987.

Wang D, Song Y, Li J, et al. Data-driven Optical Fiber Channel Modeling: a Deep Learning Approach[J]. Journal of Lightwave Technology, 2020, 38(17): 4730-4743.

Jiang R, Fu Z, Bao Y, et al. Data-driven Method for Nonlinear Optical Fiber Channel Modeling based on Deep Neural Network[J]. IEEE Photonics Journal, 2022, 14(4): 8537608.

Sidelnikov O, Redyuk A, Sygletos S, et al. Advanced Convolutional Neural Networks for Nonlinearity Mitigation in Long-haul WDM Transmission Systems[J]. Journal of Lightwave Technology, 2021, 39(8): 2397-2406.

Secondini M, Marsella D, Forestieri E.Enhanced Split-step Fourier Method for Digital Backpropagation[C]//2014 The European Conference on Optical Communication (ECOC). Cannes, France: IEEE, 2014: 6964122.

蒙建宇, 张洪波, 张敏, 等. 基于IPCA-DNN算法的光纤非线性损伤补偿[J]. 光学学报, 2021, 41(24): 2406002.

Meng J Y, Zhang H B, Zhang M, et al. Fiber Nonlinear Impairments Compensation based on IPCA-DNN Algorithm[J]. Acta Optica Sinica, 2021, 41(24): 2406002.

Karanov B, Liga G, Aref V, et al. Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing[C]//2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton). Monticello, IL, USA: IEEE, 2019: 8919850.

Häger C, Pfister H D. Nonlinear Interference Mitigation via Deep Neural Networks[DB/OL]. (2017-10-17)[2022-11-22].https://arxiv.org/abs/1710.06234https://arxiv.org/abs/1710.06234.

CHäger C, Pfister H D. Deep Learning of the Nonlinear Schrödinger Equation in Fiber-optic Communications[C]//2018 IEEE International Symposium on Information Theory (ISIT). Vail, CO, USA: IEEE, 2018: 8437734.

Shahkarami A, Yousefi M I, Jaouen Y. Efficient Deep Learning of Nonlinear Fiber-optic Communications Using a Convolutional Recurrent Neural Network[C]//2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). Pasadena, CA, USA: IEEE, 2021: 668-673.

Zhou Q, Zhang F, Yang C. AdaNN: Adaptive Neural Network-based Equalizer via Online Semi-supervised Learning[J]. Journal of Lightwave Technology, 2020, 38(16): 4315-4324.

Zhang Q, Wang Z, Duan S, et al. An Improved End-to-end Optical Transmission System based on Deep Learning[C]//2021 19th International Conference on Optical Communications and Networks (ICOCN). Qufu, China: IEEE, 2021: 9563723.

Lin B, Yang H, Wang R, et al. Convolutional Neural Network-based Signal Demodulation Method for NOMA-PON[J]. Optics Express, 2020, 28(10): 14357-14365.

Giacoumidis E, Lin Y, Blott M, et al. Real-time Machine Learning based Fiber-induced Nonlinearity Compensation in Energy-efficient Coherent Optical Networks[J]. APL Photonics, 2020, 5(4): 5140609.

更多指标>

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Deep Learning based Channel Estimation in PLC Communication

Electro-optic Modulation Programmable Optical Frequency Comb based on Deep Learning

Beam Selection Algorithm in Millimeter Wave based on GCNet

Joint PAPR Reduction Technology Enabled by Neural Network-based Coding and Companding

Research on Modulation Format Recognition Algorithm based on Machine Learning

Related Author

No data

Related Institution

Beijing Smartchip Microelectronics Technology Co., Ltd.

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications

College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications

. School of Communication and Information Engineering; b. Key Laboratory of New Generation Broadband Mobile Communication Technology, Chongqing University of Posts and Telecommunication

Lab.of Optical Fiber Sensing and Communications, University of Electronic Science and Technology

Postal code：430205
Technical support is provided by Beijing Founder electronics co., LTD 鄂ICP备2022010197 鄂公网安备 42011202002092号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：75199 Visits Today：329

⁰