Fiber Nonlinear Impairment Compensation based on Multi-source Domain Transfer Learning

CHEN Zhixuan; CAI Ju; ZHANG Hongbo; ZHANG Min; WAN Feng; DU Jie; LIU Jiao; ZHANG Qianwu

doi:10.13756/j.gtxyj.2025.230147

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Fiber Nonlinear Impairment Compensation based on Multi-source Domain Transfer Learning

更新时间：2025-02-17

- Fiber Nonlinear Impairment Compensation based on Multi-source Domain Transfer Learning
- Study on Optical Communications Issue 1, (2025)
- 作者机构：
  
  1.成都信息工程大学　通信工程学院，成都　 610225
  2.上海大学　特种光纤与光接入网省部共建重点实验室，上海　 200072
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2025.230147
  CLC： TN929
- Published：10 February 2025，
  
  Received：19 September 2023，
  
  Revised：27 September 2023，
- 稿件说明：
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陈志轩，蔡炬，张洪波，等. 基于多源域迁移学习的光纤非线性损伤补偿[J]. 光通信研究，2025(1): 230147.

Chen Z X, Cai J, Zhang H B, et al. Fiber Nonlinear Impairment Compensation based on Multi-source Domain Transfer Learning[J]. Study on Optical Communications, 2025(1): 230147.
陈志轩，蔡炬，张洪波，等. 基于多源域迁移学习的光纤非线性损伤补偿[J]. 光通信研究，2025(1): 230147. DOI： 10.13756/j.gtxyj.2025.230147.

Chen Z X, Cai J, Zhang H B, et al. Fiber Nonlinear Impairment Compensation based on Multi-source Domain Transfer Learning[J]. Study on Optical Communications, 2025(1): 230147. DOI： 10.13756/j.gtxyj.2025.230147.

摘要

【目的】

近年来基于神经网络（NN）的均衡器在光纤非线性损伤补偿中被广泛使用，但在实际应用中，需要消耗大量资源对NN进行再训练以适应新环境下的光通信系统。迁移学习通过将初始系统（源域）训练的NN模型中的一部分参数应用于新环境（目标域）下的NN模型，仅需使用少量的训练数据即可实现目标域模型的快速重构，但是该方法需要在所有源域中找到最佳源域进行迁移以获得良好的性能，且当目标域变化后需重新寻找最佳源域，这会消耗大量的训练资源。

【方法】

为解决此问题，文章提出了一种基于多源域迁移学习的方法，该方法将卷积神经网络（CNN）和双向长短期记忆（BiLSTM）作为均衡器，通过特定源域训练和多源域训练两个过程交替更新网络参数，然后针对新环境下的光通信系统进行微调，只需使用少量的初始训练数据即可适应传输系统的变化，且无需寻找最佳源域即可实现良好的性能。

【结果】

文章在5通道50-GBaud波分复用（WDM）双偏振16阶正交幅度调制（DP-16QAM）的光传输系统中进行了仿真验证，综合数值仿真结果表明，仅需使用35%的目标域数据进行多源域迁移学习，即可具有比再训练方法更好的性能。同时，与再训练和单源域迁移学习相比，多源域迁移学习的

因子分别提高了0.82和0.18 dB。

【结论】

因此，多源域迁移学习方案更适合于实际的光通信系统。

Abstract

【Objective】

In recent years

equalizer based on Neural Network (NN) has been widely used in optical fiber nonlinear impairment compensation. However

in practical application

it needs to consume a lot of resources to retrain NN to adapt to optical communication system in new environment. Transfer learning applies some parameters of the NN model trained by the initial system (source domain) to the NN model in the new environment (target domain). Only a small amount of training data is needed to achieve rapid reconstruction of the target domain model. However

this method needs to find the best source domain in all source domains for migration to obtain good performance. When the target domain changes

it is necessary to find the best source domain again

which will consume a lot of training resources. This work suggests a solution based on multi-source domain transfer learning to solve this issue.

【Methods】

This method employs Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) as equalizers. It alternately updates network parameters through two processes: specific source domain training and multi-source domain training. Subsequently

the optical communication system in the new environment is fine-tuned

allowing it to adapt to changes in the transmission system using only a small amount of initial training data. Moreover

good performance can be achieved without the need to search for the optimal source domain.

【Results】

A 5-channel 50-GBaud Wavelength Division Multiplexing (WDM) Dual-Polarization 16-order Quadrature Amplitude Modulation (DP-16QAM) optical transmission system is simulated to verify the effectiveness of the proposed method. The numerical simulation results show that the multi-source domain transfer learning outperforms the retraining method when using just 35% of the target domain data. Meanwhile

the

-factor of multi-source domain transfer learning are improved by 0.82 and 0.18 dB

respectively

in compared with retraining and single source domain transfer learning.

【Conclusion】

Therefore

the multi-source transfer learning scheme is suitable for practical optical communication systems.

关键词

Keywords

references

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