QoT推理与自信决策的深度分位数回归

T. Panayiotou, Hafsa Maryam, G. Ellinas
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引用次数: 2

摘要

本文研究了深度分位数回归在光网络传输质量(QoT)估计和准确决策中的应用。分位数回归应用于近似QoT模型,该模型能够根据预定义的确定性水平推断任何未来光路的QoT界限,以进行自信的决策,而无需在决策时考虑传统的边际。结果表明,与传统的基于边缘的决策方法相比,分位数回归以一种判别的方式自动解释了这种边缘,导致了显著的边缘减少,随后对未建立光路的QoT进行了更准确的推断。具体来说,QoT估计的深度分位数回归确保了QoT不足的光路被准确识别和拒绝,同时也能正确识别QoT充足的光路,使其成为光网络规划的可靠决策工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Quantile Regression for QoT Inference and Confident Decision Making
This work examines deep quantile regression for quality-of-transmission (QoT) estimation and accurate decision making in optical networks. Quantile regression is applied to approximate QoT models capable of inferring QoT bounds for any future lightpath, according to a predefined level of certainty, for confident decision making, without the need to consider traditional margins at decision time. It is shown, that quantile regression automatically accounts for such margins, in a discriminative fashion, leading to a significant margin reduction and subsequently to more accurate inference of the QoT of unestablished lightpaths, when compared to the traditional margin-based decision approaches. Specifically, deep quantile regression for QoT estimation ensures that lightpaths with insufficient QoT will be accurately identified and rejected, while also identifying correctly lightpaths with sufficient QoT, making it a confident decision making tool for the planning of optical networks.
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