光学频率梳噪声表征使用机器学习

45th European Conference on Optical Communication (ECOC 2019) Pub Date : 2019-04-23 DOI:10.1049/cp.2019.0889

Giovanni Brajato, Lars Lundberg, V. Torres‐Company, D. Zibar

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引用次数: 5

摘要

基于贝叶斯滤波框架和期望最大化算法，通过数值和实验验证了一种新的频率梳噪声表征方法。与传统方法相比，该工具在均方误差意义上具有统计最佳性，可在宽信噪比范围内工作，并提供更准确的噪声估计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Optical frequency comb noise characterization using machine learning

A novel tool, based on Bayesian filtering framework and expectation maximization algorithm, is numerically and experimentally demonstrated for accurate frequency comb noise characterization. The tool is statistically optimum in a mean-square-error-sense, works at wide range of SNRs and offers more accurate noise estimation compared to conventional methods.

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45th European Conference on Optical Communication (ECOC 2019)

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