Addressing Traffic Prediction Uncertainty in Multi-Period Planning Optical Networks

被引：0

作者：

Panayiotou, Tania ^{[1
]}

Ellinas, Georgios

机构：

[1] Univ Cyprus, Dept Elect & Comp Engn, CY-1678 Nicosia, Cyprus

来源：

2022 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXHIBITION (OFC) | 2022年

基金：

欧盟地平线“2020”;

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Deep-quantile regression is leveraged to capture traffic prediction uncertainty over future network planning intervals. We show that quantile predictions, acting as discriminative margins, result to significant spectrum savings compared to empirically estimated myopic margins considered. (C) 2022 The Author(s)

引用

页数：3

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