长距离光纤通信系统的非线性损伤补偿方法

袁瑛; 沈袁勋; 沈平

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长距离光纤通信系统的非线性损伤补偿方法

光信息技术 | 更新时间：2025-10-09

- 长距离光纤通信系统的非线性损伤补偿方法
- Nonlinear impairment compensation method for Long-haul Fiber-optic communication system
- 光学技术 2025年51卷第4期页码：400-407
- 作者机构：
  
  1.湖北职业技术学院信息工程学院，湖北孝感 432000
  2.东北大学计算机与通信工程学院，河北秦皇岛 066004
  3.湖北职业技术学院教务处，湖北孝感 432000
- 作者简介：
  
  袁瑛（1977— ），女，副教授，硕士，从事数据库技术、软件技术方面的研究。allenincntt@163.com
- 基金信息：
  
  中国高校产学研创新基金-新一代信息技术创新项目(2023IT160)
- DOI：
  中图分类号： TP394.1;TH691.9
- 收稿：2025-03-26，
  
  修回：2025-05-17，
  
  纸质出版：2025-07-20
- 稿件说明：
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袁瑛,沈袁勋,沈平.长距离光纤通信系统的非线性损伤补偿方法[J].光学技术,2025,51(04):400-407.

YUAN Ying,SHEN Yuanxun,SHEN Ping.Nonlinear impairment compensation method for Long-haul Fiber-optic communication system[J].OPTICAL TECHNIQUE,2025,51(04):400-407.
袁瑛,沈袁勋,沈平.长距离光纤通信系统的非线性损伤补偿方法[J].光学技术,2025,51(04):400-407. DOI：

YUAN Ying,SHEN Yuanxun,SHEN Ping.Nonlinear impairment compensation method for Long-haul Fiber-optic communication system[J].OPTICAL TECHNIQUE,2025,51(04):400-407. DOI：

摘要

随着传输速率与传输距离的增加，非线性损伤对长距离光纤通信系统性能的影响愈发显著，严重制约了系统性能。为了缓解长距离光纤通信中非线性损伤的综合效应，提出一种基于双分支混合神经网络的长距离光通信非线性损伤补偿方法。该模型利用信道记忆从时域捕捉信道损伤的全局特征，利用历史信息优化当前符号的特征学习；通过基于卷积神经网络的实数分支捕获星座图中符号的空间特征，并采用通道注意力机制和空间注意力机制增强模型对非线性损伤相关空间特征的学习能力；通过复数分支捕获复数符号相位与幅度变化的隐含信息，从而有效补偿非线性损伤的综合效应。以自相位调制和交叉相位调制为主要非线性效应建立了数值仿真模型，仿真结果表明，在不同发射光功率、传输距离和符号率条件下，所提方法均能实现较低的误码率，并表现出良好的稳定性。

Abstract

With the increase in transmission rate and transmission distance

the impact of nonlinear impairments on the performance of long-haul fiber-optic communication systems becomes increasingly significant

severely restricting overall system performance. In order to mitigate integrated effects of nonlinear impairment for the long-haul fiber-optic communication system

a dual-branch hybrid neural network-based nonlinear impairment compensation method for the long-haul optical communication system is proposed. Firstly

the channel memory is leveraged by the model to capture the global feature of channel impairments in the time domain

so as to optimize feature learning of current symbols through historical information; Secondly

the spatial features of symbols in the constellation diagram are captured by the real-valued branch based on convolutional neural networks

and the channel attention mechanism and the spatial attention mechanism are adopted to enhance the learning capability of the model for nonlinear impairment-related spatial features; Finally

the latent information of phase and amplitude variations of complex symbols is captured by the complex-valued branch

thereby the combined effects of nonlinear impairments are effectively compensated. Numerical simulation is established with self-phase modulation and cross-phase modulation as the primary nonlinear effects

simulation results demonstrate that the proposed method achieves a lower bit error rate and exhibits stability under varying conditions of transmitted optical power

transmission distance and symbol rate.

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references

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