Multi-input neural channel waveform model for optical fiber WDM transmission based on Volterra series transfer function.

IF 3.3 2区 物理与天体物理 Q2 OPTICS
Optics express Pub Date : 2025-09-08 DOI:10.1364/OE.563482
Xingchen He, Lianshan Yan, Lin Jiang, Jihui Sun, Anlin Yi, Wei Pan, Bin Luo
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引用次数: 0

Abstract

Accurate waveform modeling of optical fiber channels is essential for the design, optimization, and management of wavelength-division multiplexing (WDM) systems in optical communication networks. To address the computational inefficiencies of the traditional split-step Fourier method (SSFM), deep learning has achieved significant advancements in this field. However, current deep learning based channel models take only transmitted signals as inputs, achieving good generalization for system parameters that can be derived from the signal waveform. For system parameters such as baud rate, dispersion and nonlinearity coefficients that cannot be extracted from the waveform, any variation in these parameters necessitate retraining the model, thereby limiting its flexibility. Here, we build upon the existing physics-based Volterra series transfer function algorithm and employ neural network parameterization in the frequency domain (NN-VS) to achieve high-accuracy and robust generalization modeling of WDM channels with support for multi-parameter inputs. We evaluated the performance of NN-VS in simulations of a 40-channel 600 km and a 5-channel 1200 km WDM system. Under various baud rates, dispersion coefficients, and nonlinearity coefficients, the proposed NN-VS scheme achieved an average Q-factor error of less than 0.15 dB at the optimal launch power. Furthermore, NN-VS demonstrates superior computational efficiency compared to SSFM, achieving transmission in a 40-channel WDM scenario with less than 2% of the real multiplications while delivering millisecond-scale runtime on a GPU.

基于Volterra串联传递函数的光纤WDM传输多输入神经通道波形模型。
光纤信道的精确波形建模对于光通信网络中波分复用系统的设计、优化和管理至关重要。为了解决传统的分步傅里叶方法(SSFM)计算效率低下的问题,深度学习在这一领域取得了重大进展。然而,目前基于深度学习的信道模型仅将传输信号作为输入,对可以从信号波形中导出的系统参数进行了很好的泛化。对于无法从波形中提取的系统参数,如波特率、色散和非线性系数,这些参数的任何变化都需要重新训练模型,从而限制了模型的灵活性。本文在现有的基于物理的Volterra系列传递函数算法的基础上,利用频域神经网络参数化(NN-VS)实现了支持多参数输入的WDM信道的高精度和鲁棒泛化建模。我们在40通道600公里和5通道1200公里WDM系统的仿真中评估了NN-VS的性能。在不同波特率、色散系数和非线性系数下,在最佳发射功率下,NN-VS方案的平均q因子误差小于0.15 dB。此外,与SSFM相比,NN-VS显示出卓越的计算效率,在40通道WDM场景中实现传输,实际乘法不到2%,同时在GPU上提供毫秒级运行时间。
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来源期刊
Optics express
Optics express 物理-光学
CiteScore
6.60
自引率
15.80%
发文量
5182
审稿时长
2.1 months
期刊介绍: Optics Express is the all-electronic, open access journal for optics providing rapid publication for peer-reviewed articles that emphasize scientific and technology innovations in all aspects of optics and photonics.
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