一种基于egn辅助机器学习的网络规划QoT估计方法

2021 European Conference on Optical Communication (ECOC) Pub Date : 2021-09-13 DOI:10.1109/ECOC52684.2021.9606064

Jasper Müller, S. Patri, T. Fehenberger, C. M. Machuca, H. Griesser, J. Elbers

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引用次数: 4

摘要

提出了一种基于预先计算的每通道SCI的机器学习模型。由于其优于封闭形式GN的精度，在端到端链路优化中平均信噪比增益为1.1 dB，并且在网络规划场景中满足流量请求所需光路减少40%。

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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A QoT Estimation Method using EGN-assisted Machine Learning for Network Planning Applications

An ML model based on precomputed per-channel SCI is proposed. Due to its superior accuracy over closed-form GN, an average SNR gain of 1.1 dB in an end-to-end link optimization and a 40% reduction in required lightpaths to meet traffic requests in a network planning scenario are shown.

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来源期刊

2021 European Conference on Optical Communication (ECOC)

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