2019 journal article

A machine learning approach to mitigating fragmentation and crosstalk in space division multiplexing elastic optical networks


By: Y. Xiong*, Y. Yang*, Y. Ye* & G. Rouskas n

author keywords: Space division multiplexing elastic optical networks; Spectrum fragmentation; Machine learning; Crosstalk
TL;DR: This paper uses an Elman neural network to forecast traffic demands, and uses a two-dimensional rectangular packing model to allocate spectrum so as to decrease unnecessary spectrum fragmentation and increase resource utilization in SDM-EONs. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: June 4, 2019

2016 conference paper

Exploiting SDN principles for extremely fast restoration in elastic optical datacenter Networks

2016 ieee global communications conference (globecom).

By: Y. Xiong*, Y. Li*, X. Dong*, Y. Gao & G. Rouskas*

TL;DR: The experimental results demonstrate that the proposed scheme can achieve fast recovery with low blocking probability while maintaining high spectrum efficiency, and compared with existing restoration schemes, average recovery time is improved by up to 28%. (via Semantic Scholar)
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018

Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.

Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.