Beta Training Dataset

<p>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.</p> <p><br></p> <p>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.</p> <p><br></p> <p>[1]<br> 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]’, <em>J. Opt. Commun. Netw., JOCN</em>, vol. 13, no. 8, pp. D53–D67, Aug. 2021, doi: <a href="https://doi.org/10.1364/JOCN.423490" target="_blank">10.1364/JOCN.423490</a>.<br> <br>  </p> <p><br></p>