The key goal in optical network design is to introduce intelligence in the network and deliver capacity when and where it is needed. It is critical to understand the dependencies between network topology properties and the achievable network throughput. Real topology data of optical networks are scarce, and often large sets of synthetic graphs are used to evaluate their performance including proposed routing algorithms. These synthetic graphs are typically generated via the Erdos–Renyi (ER) and Barabasi–Albert (BA) models. Both models lead to distinct structural properties of the synthetic graphs, including degree and diameter distributions. In this paper, we show that these two commonly used approaches are not adequate for the modeling of real optical networks. The structural properties of optical core networks are strongly influenced by internodal distances. These, in turn, impact the signal-to-noise ratio, which is distance dependent. The analysis of optical network performance must, therefore, include spatial awareness to better reflect the graph properties of optical core network topologies. In this work, a new variant of the BA model, taking into account the internodal signal-to-noise ratio, is proposed. It is shown that this approach captures both the effects of graph structure and physical properties to generate better networks than traditional methods. The proposed model is compared to spatially agnostic approaches, in terms of the wavelength requirements and total information throughput, and highlights how intelligent choices can significantly increase network throughputs while saving fiber.

中文翻译：

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做出智能拓扑设计选择：了解光网络中的结构和物理性能影响 [邀请]

光网络设计的关键目标是在网络中引入智能并在需要的时间和地点提供容量。了解网络拓扑属性与可实现的网络吞吐量之间的依赖关系至关重要。光网络的真实拓扑数据很少，通常使用大量合成图来评估其性能，包括提出的路由算法。这些合成图通常是通过 Erdos-Renyi (ER) 和 Barabasi-Albert (BA) 模型生成的。两种模型都导致合成图的不同结构特性，包括度数和直径分布。在本文中，我们表明这两种常用方法不足以对真实光网络进行建模。光核心网的结构特性受节点间距离的影响很大。这些反过来又会影响信噪比，而信噪比与距离有关。因此，光网络性能的分析必须包括空间感知，以更好地反映光核心网络拓扑的图形特性。在这项工作中，考虑了节点间信噪比，提出了 BA 模型的新变体。结果表明，这种方法同时捕获了图结构和物理属性的影响，以生成比传统方法更好的网络。所提出的模型在波长要求和总信息吞吐量方面与空间不可知方法进行了比较，并强调了智能选择如何在节省光纤的同时显着增加网络吞吐量。