Chromatic Dispersion and Fiber Nonlinear Impairment Compensation based on LDBP

CHEN Zhixuan; CAI Ju; ZHANG Hongbo; ZHANG Min; MENG Jianyu; ZHANG Qianwu

doi:10.13756/j.gtxyj.2024.230047

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Chromatic Dispersion and Fiber Nonlinear Impairment Compensation based on LDBP

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- Chromatic Dispersion and Fiber Nonlinear Impairment Compensation based on LDBP
- Study on Optical Communications Issue 4, Pages: 23004701-23004705(2024)
- 作者机构：
  
  1.成都信息工程大学　通信工程学院，成都　610225
  2.四川大学，成都　610065
  3.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.2024.230047
  CLC： TN929
- Published：10 August 2024，
  
  Received：08 August 2023，
  
  Revised：20 August 2023，
- 稿件说明：
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陈志轩，蔡炬，张洪波，等. 基于LDBP的色散和光纤非线性损伤补偿[J]. 光通信研究，2024(4)：230047.

Chen Z X, Cai J, Zhang H B, et al. Chromatic Dispersion and Fiber Nonlinear Impairment Compensation based on LDBP [J]. Study on Optical Communications, 2024(4)：230047.
陈志轩，蔡炬，张洪波，等. 基于LDBP的色散和光纤非线性损伤补偿[J]. 光通信研究，2024(4)：230047. DOI： 10.13756/j.gtxyj.2024.230047.

Chen Z X, Cai J, Zhang H B, et al. Chromatic Dispersion and Fiber Nonlinear Impairment Compensation based on LDBP [J]. Study on Optical Communications, 2024(4)：230047. DOI： 10.13756/j.gtxyj.2024.230047.

摘要

【目的】

在长距离光通信系统中，由于克尔非线性、色散和放大自发辐射噪声之间的相互作用，使用传统的数字信号处理（DSP）来补偿光纤非线性损伤十分困难。机器学习算法可以用于在传统DSP的基础上对信号进行进一步处理，以缓解光纤非线性损伤并提高长距离光纤传输系统的性能。

【方法】

为了更好地补偿光纤传输中色散和非线性损伤，文章将传统的数字反向传播（DBP）算法和深度神经网络（DNN）相结合，将DBP中的线性步长和非线性步长作为一个神经元，即将线性步长看作DNN的权重矩阵，非线性步长看作激活函数，并将DSP作为DNN的静态层，提出了一种基于DNN的自学习DBP（LDBP）算法。

【结果】

为了验证文章所提算法的可行性，在单通道偏振复用16阶正交幅度调制（QAM）的光传输系统中进行了仿真。综合数值仿真结果表明，与线性均衡相比较，每跨一步的LDBP算法将最佳发射功率从－2 dBm提高至1 dBm。同时，与相同计算复杂度的DBP算法相比，LDBP算法在1 200 km的传输距离下，信噪比（SNR）提高了0.82 dB。此外，与线性均衡和相同计算复杂度的DBP算法相比，LDBP算法所对应传输系统的SNR随着传输距离的增加，下降速度更加缓慢，并且该算法可以在无需预知链路参数的情况下工作，具有普适性和鲁棒性。

【结论】

文章所提LDBP算法与传统DBP算法相比，更加适用于实际的长距离相干光通信系统。

Abstract

【Objective】

In long-distance optical communication systems

compensating fiber nonlinear impairment through traditional Digital Signal Processing (DSP) is difficult due to intractable interactions between Kerr nonlinearity

chromatic dispersion and amplified spontaneous emission noise. Machine learning algorithm can be used to further process signals on the basis of traditional DSP to mitigate fiber nonlinear impairment and improve long-distance transmission performance.

【Methods】

In this paper

the traditional Digital Back Propagation (DBP) algorithm is combined with Deep Neural Network (DNN)

where the linear step size and nonlinear step size in DBP are taken as one neuron. It means that the linear step size is taken as the weight matrix of DNN

the nonlinear step size is taken as the activation function

and DSP is taken as the static layer of DNN. A DNN-based Learned Digital Back Propagation (LDBP) algorithm is proposed.

【Results】

In order to verify the feasibility of the proposed LDBP algorithm

the simulation was carried out in a single-channel polarization division multiplexing 16-ary Quadrature Amplitude Modulation (QAM) optical transmission system. The numerical simulation results demonstrate that the 1-step-per-span LDBP algorithm improves the optimal launched power from -2 dBm to 1 dBm in compared to linear equalization. Meanwhile

compared with DBP with the same computational complexity

the proposed algorithm improves Signal-to-Noise Ratio (SNR) by 0.82 dB at the transmission distance of 1 200 km. In addition

compared with DBP with the same computational complexity and linear equalization

the SNR of the transmission system corresponding to LDBP method decreases more slowly with the increase of transmission distance

and the algorithm can work without knowing the link parameters

showing the characteristic of universality and robustness.

【Conclusion】

The proposed LDBP algorithm is more suitable for practical long-distance coherent optical communication system than the traditional DBP algorithm.

关键词

色散补偿非线性损伤补偿深度神经网络自学习数字反向传播

Keywords

chromatic dispersion compensationnonlinear impairment compensationDNNLDBP

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