基于信号幅度均值和方差的光信噪比监测技术

陈芳; 李子凡; 朱彦源; 李伯中; 龙函; 吴剑军; 段明雄

doi:10.13756/j.gtxyj.2024.230139

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基于信号幅度均值和方差的光信噪比监测技术

光通信系统与网络技术 | 更新时间：2024-12-05

- 基于信号幅度均值和方差的光信噪比监测技术
- An OSNR Monitoring Scheme based on Mean and Variance Value of Signal Amplitude
- 光通信研究 2024年第6期页码：23013901-23013905
- 作者机构：
  
  1.国家电网有限公司信息通信分公司，北京　100761
  2.武汉光迅科技股份有限公司，武汉　430205
- 作者简介：
  
  陈芳（1982-），女，河北石家庄人。高级工程师，硕士，主要研究方向为光纤通信系统。
  龙函，工程师。E-mail：1813697612@qq.com
- 基金信息：
- DOI：10.13756/j.gtxyj.2024.230139
  中图分类号： TN929.1
- 纸质出版日期：2024-12-10，
  
  收稿日期：2023-09-06，
  
  修回日期：2023-10-07，
- 稿件说明：
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陈芳，李子凡，朱彦源，等. 基于信号幅度均值和方差的光信噪比监测技术[J]. 光通信研究，2024(6):230139.

Chen F, Li Z F, Zhu Y Y, et al. An OSNR Monitoring Scheme based on Mean and Variance Value of Signal Amplitude [J]. Study on Optical Communications, 2024(6): 230139.
陈芳，李子凡，朱彦源，等. 基于信号幅度均值和方差的光信噪比监测技术[J]. 光通信研究，2024(6):230139. DOI： 10.13756/j.gtxyj.2024.230139.

Chen F, Li Z F, Zhu Y Y, et al. An OSNR Monitoring Scheme based on Mean and Variance Value of Signal Amplitude [J]. Study on Optical Communications, 2024(6): 230139. DOI： 10.13756/j.gtxyj.2024.230139.

摘要

【目的】

光信噪比（OSNR）是通信系统性能优劣的重要评判依据，对于光纤通信系统，只有满足接收端OSNR大于接收机OSNR的接收容限才能保证系统稳定运行，并且接收机OSNR值越大，说明系统稳定性越好，因此对传输系统OSNR数值的监测十分重要，而OSNR的实时准确监测一直是光纤通信系统中的难题。

【方法】

为实现传输系统简单、高效且高精准度的OSNR监测，文章基于信号幅度直方图的均值和方差，结合深度神经网络（DNN），提出了一种高精确性的OSNR监测方案，该方案通过接收端信号幅度直方统计图不同区域的均值和方差，利用DNN提取信号幅度直方图随OSNR变化的关联特征，从而实现对系统OSNR的监测。

【结果】

采用该监测方案对正交相移键控（QPSK）/16正交幅度调制（QAM）/32QAM 3种调制格式的传输系统接收端OSNR进行实验监测研究，结果显示，3种调制格式信号在背靠背传输系统中，接收端OSNR估计值和实际值最大平均绝对误差分别为0.048、0.140和0.180 dB；在分别经过2 000、800和800 km光纤传输后，3种调制格式传输系统接收端OSNR估计值和实际值最大平均绝对误差分别为0.082、0.250和0.630 dB，对比不传纤系统，经过光纤传输后系统OSNR估计值平均误差不超过0.5 dB，说明该OSNR监测方案可实现传输系统性能的精准监测且不受光纤线路的影响。

【结论】

经实验验证，文章所提基于信号幅度直方图的均值和方差的DNN OSNR监测方法，与实际系统接收端OSNR最大误差不超过1 dB，说明文章所提方案能准确预测系统接收端OSNR值。

Abstract

【Objective】

Optical Signal-to-Noise Ratio (OSNR) is an important criterion for evaluating the performance of communication systems. For optical fiber communication systems

stable operation can only be ensured if the OSNR at the receiver end exceeds the receiver's OSNR receiving tolerance. The larger the value

the better the system stability. Therefore

monitoring the OSNR value of the transmission system is very important

and real-time and accurate monitoring of OSNR has always been a challenge in optical fiber communication systems.

【Methods】

To achieve a simple

efficient

and high-precision OSNR monitoring

based on the mean and variance of the signal amplitude histogram

combined with a Deep Neural Network (DNN)

the article proposes a highly accurate OSNR monitoring method. This method utilizes the mean and variance of different regions of the received signal amplitude histogram to extract correlation features of the signal amplitude histogram as it changes with OSNR using the DNN

thereby enabling the monitoring of the system's OSNR.

【Results】

This monitoring scheme is used to experimentally monitor the receiver OSNR of Quadrature Phase Shift Keying (QPSK) /16 Quadrature Amplitude Modulation (QAM) /32QAM transmission systems with three different modulation formats. The results show that the maximum standard errors of the estimated and actual OSNR values of the three different modulation formats in the back-to-back transmission system are 0.048

0.140

and 0.180 dB without fiber transmission. After passing through 2 000

800 and 800 km optical fiber transmission

the maximum standard errors of the estimated and actual OSNR values of the receiving end of the three different modulation formats transmission systems are 0.082

0.250 and 0.630 dB

respectively. Compared with the non fiber transmission system

the maximum error of the estimated OSNR values of the system after optical fiber transmission does not exceed 0.5 dB. This indicates that the OSNR monitoring scheme can achieve precise monitoring of transmission system performance and is not affected by fiber optic lines.

【Conclusion】

The DNN OSNR monitoring method based on the mean and variance of the signal amplitude histogram proposed in this paper has been experimentally verified. The maximum error between this scheme and the actual system receiver OSNR does not exceed 1dB

indicating that this scheme can accurately predict the system receiver OSNR value.

关键词

光纤通信系统光信噪比监控深度神经网络信号幅度

Keywords

optical fiber communication systemOSNR monitoringDNNsignal amplitude

references

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李鸿, 刘武, 罗鸣. 基于机器学习的光网络监测与优化方法[J]. 光通信研究, 2024(3):230046.

Li H, Liu W, Luo M. Optical Network Monitoring and Optimization Methods based on Machine Learning[J]. Study on Optical Communications, 2024(3):230046.

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