Optical Performance Monitoring Technology of IMDD System based on Deep Neural Network

LIU Jun; LI Bo-zhong; CHENG Fang; LI Zi-fan; GUO Ying; SUN Yu-xiao; DENG Cun-xue; ZHANG Ru-yi; WANG Ying-xu

doi:10.13756/j.gtxyj.2023.01.004

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Optical Performance Monitoring Technology of IMDD System based on Deep Neural Network

更新时间：2023-01-16

- Optical Performance Monitoring Technology of IMDD System based on Deep Neural Network
- Study on Optical Communications Issue 1, Pages: 42-46(2023)
- 作者机构：
  
  国家电网有限公司信息通信分公司，北京　100761
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2023.01.004
  CLC： TN929.11
- Published：10 February 2023，
  
  Received：09 May 2022，
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刘军，李伯中，陈芳，等.基于深度神经网络的IMDD系统光性能监测技术[J].光通信研究，2023(1)：42-46

LIU J, LI B Z, CHENG F, et al.Optical Performance Monitoring Technology of IMDD System Based on Deep Neural Network[J]. Study on Optical Communications, 2023(1):42-46
刘军，李伯中，陈芳，等.基于深度神经网络的IMDD系统光性能监测技术[J].光通信研究，2023(1)：42-46 DOI： 10.13756/j.gtxyj.2023.01.004.

LIU J, LI B Z, CHENG F, et al.Optical Performance Monitoring Technology of IMDD System Based on Deep Neural Network[J]. Study on Optical Communications, 2023(1):42-46 DOI： 10.13756/j.gtxyj.2023.01.004.

摘要

在先进高速光纤通信系统中，密集波分复用技术的引入使得信号频谱间隔越来越窄，传统的带外光信噪比（OSNR）监测技术不再准确，需要进一步研究低成本的带内OSNR监测方案。文章提出了一种基于深度神经网络（DNN）的直调直检（IMDD）系统链路OSNR监测方案，采用550 000组数据集训练的5层DNN结构，在5～15 dB范围内，成功估计出了2 GBaud开关键控（OOK）信号的OSNR，平均绝对误差（MAE）小于0.8 dB。文章所提方案有望作为一种高效低成本方案，助力光网络智能运维。

Abstract

In advanced high-speed fiber optic communication systems

due to the introduction of dense wavelength division multiplexing technology

the signal spectral interval is getting narrower and narrower

and the traditional out-of-band Optical Signal-to-Noise Ratio (OSNR) monitoring technology is no longer accurate. Therefore

further study is required in the low-cost in-band OSNR monitoring scheme. A Deep Neural Network (DNN) link OSNR monitoring scheme for Intensity-Modulation and Direct Detection (IMDD) system is proposed. We used a 5-layer DNN trained from 550 000 datasets to successfully estimate the OSNR of the 2 GBaud On-Off Key (OOK) signal in the range of 5 to 15 dB

and the Mean Absolute Error (MAE) is less than 0.8 dB.

关键词

深度神经网络光信噪比光性能监测直调直检

Keywords

DNNOSNRoptical performance monitoringIMDD

references

Pan Z, Yu C, Willner A E. Optical Performance Monitoring for the Next Generation Optical Communication Networks[J]. Optical Fiber Technology, 2010, 16(1): 20-45.

Khan F N, Lu C, Lau A. Optical Performance Monitoring in Fiber-optic Networks Enabled by Machine Learning Techniques[C]//Optical Fiber Communications Conference and Exposition. San Diego, CA, USA: IEEE, 2018: M2F.3.

Wang D, Wang M, Zhang M, et al. Cost-effective and Data Size-adaptive OPM at Intermediated Node Using Convolutional Neural Network-based Image Processor[J]. Optics Express, 2019, 27(7): 9403-9419.

Tanimura T, Hoshida T, Rasmussen J, et al. OSNR Monitoring by Deep Neural Networks Trained with Asynchronously Sampled Data[C]//Optoelectronics & Communications Conference. Niigata, Japan: IEEE, 2016: TuB3-5.

Wang D, Zhang M, Li Z, et al. Modulation Format Recognition and OSNR Estimation Using CNN-based Deep Learning[J]. IEEE Photonics Technology Letters, 2017, 29(19): 1667-1670.

Wang D, Zhang M, Li J, et al. Intelligent Constellation Diagram Analyzer Using Convolutional Neural Network-based Deep Learning[J]. Optics Express, 2017, 25(15): 17150.

Wang C, Fu S, Xiao Z, et al. Long Short-Term Memory Neural Network (LSTM-NN) Enabled Accurate Optical Signal-to-Noise Ratio (OSNR) Monitoring[J]. Journal of Lightwave Technology, 2019, 37(16): 4140-4146.

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