2026-05-28T22:36:11Z https://api.figshare.com/v2/oai
oai:figshare.com:article/21695687 2022-12-08T15:56:41Z category_26401 portal_549 item_type_3 month_year_12_2022
10.5522/04/21695687.v1 https://figshare.com/articles/dataset/Beta_Training_Dataset/21695687 https://ndownloader.figshare.com/files/38478437 Matzner, Robin Robin Matzner 0000-0003-0278-8500 <![CDATA[Beta Training Dataset]]> Optical fibre communication systems and technologies training dataset Optical Networks and Systems 2022-12-08 2022-12-08 Dataset 2022 University College London This training dataset included optical network topologies that are generated via SNR-BA method [1] with nodes scattered uniformly randomly over a grid the size of the north american continent. Here there is a minimum radius that is adhered to (100km) between the nodes. The nodes are between scales of 25-45 nodes.


The routings of the network are computed under uniform bandwidth conditions with the first-fit k-shortest-path (FF-kSP) algorithm and sequential loading (SL) until the maximum state of the network is found at zero blocking. The Gaussian noise (GN) model is used to calculate the signal-to-noise ratio of paths and the total throughput of the network. This throughput is given as a training label.


[1]
R. Matzner, D. Semrau, R. Luo, G. Zervas, and P. Bayvel, ‘Making intelligent topology design choices: understanding structural and physical property performance implications in optical networks [Invited]’, J. Opt. Commun. Netw., JOCN, vol. 13, no. 8, pp. D53–D67, Aug. 2021, doi: 10.1364/JOCN.423490.

 


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