基于高斯过程的数字反向传播光网络物理层参数优化

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2023-08-10 DOI:10.1017/dce.2023.15
Josh W. Nevin, E. Sillekens, Ronit Sohanpal, L. Galdino, Sam Nallaperuma, P. Bayvel, S. Savory
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引用次数: 0

摘要

摘要本文提出了一种优化光纤网络性能的新方法,通过确定衰减、非线性和色散参数的理想值来获得数字反向传播(DBP)的信噪比(SNR)增益。我们的方法使用高斯过程回归,一种概率机器学习技术,创建一个计算效率高的模型,将这些参数映射到应用DBP后得到的信噪比。然后,我们使用简单同调全局优化来找到在一组先验边界内高斯过程模型产生最大信噪比的参数值。这种方法根据接收机的DBP增益优化了参数。我们通过模拟和实验测试证明了我们方法的有效性,实现了色散、非线性和衰减参数的最佳估计。我们的方法还强调了传统的一次网格搜索方法的局限性,并强调了该技术的可解释性。该方法在工程上有广泛的应用,可用于优化光网络以外的各种系统的性能。
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Optical network physical layer parameter optimization for digital backpropagation using Gaussian processes
Abstract We present a novel methodology for optimizing fiber optic network performance by determining the ideal values for attenuation, nonlinearity, and dispersion parameters in terms of achieved signal-to-noise ratio (SNR) gain from digital backpropagation (DBP). Our approach uses Gaussian process regression, a probabilistic machine learning technique, to create a computationally efficient model for mapping these parameters to the resulting SNR after applying DBP. We then use simplicial homology global optimization to find the parameter values that yield maximum SNR for the Gaussian process model within a set of a priori bounds. This approach optimizes the parameters in terms of the DBP gain at the receiver. We demonstrate the effectiveness of our method through simulation and experimental testing, achieving optimal estimates of the dispersion, nonlinearity, and attenuation parameters. Our approach also highlights the limitations of traditional one-at-a-time grid search methods and emphasizes the interpretability of the technique. This methodology has broad applications in engineering and can be used to optimize performance in various systems beyond optical networks.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
期刊最新文献
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