Gamma Training Dataset

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 55-100 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.

本训练数据集包含通过信噪比-贝叶斯算法（SNR-BA）[1]生成的光网络拓扑结构，节点在北美大陆大小的网格中均匀随机分布。节点间保持最小半径限制（100公里）。节点数量介于55至100之间。网络路由在均匀带宽条件下，通过首次适配k最短路径（FF-kSP）算法和连续加载（SL）计算，直至网络达到零阻塞的最大状态。使用高斯噪声（GN）模型计算路径的信号噪声比和网络的总吞吐量。该吞吐量作为训练标签提供。[1] R. Matzner, D. Semrau, R. Luo, G. Zervas, 和 P. Bayvel，‘构建智能拓扑设计选择：理解结构性和物理属性性能影响在光网络中的重要性[邀请]’，《光学通信网络杂志》（J. Opt. Commun. Netw.），第13卷，第8期，第D53–D67页，2021年8月，DOI：10.1364/JOCN.423490。