CIF: Small: Computational Tools for Bounding Network Capacities

通信网络越来越成为世界金融、教育和政府机构所依赖的支柱。令人惊讶的是，人们对当前通信网络可以承载多少信息知之甚少。 由于缺乏描述大型复杂网络“能力”的工具，因此难以确定如何提高当前通信网络的能力或为未来设计更好的网络。 该研究涉及开发新的系统化策略，用于构建表征大型网络容量的计算工具，该研究涉及首先将网络分解为单个组件，然后用更简单的模型替换这些组件，最后使用计算工具来约束建模网络的容量，从而约束复杂网络的容量。 研究人员通过推导所研究的每个组件的上边界和下边界模型来创建组件模型库。 与用于表征各个通道的行为的现有机制不同，这些上边界和下边界模型捕获各个组件的全部行为范围。 因此，任何网络的容量从上到下由另一个网络的容量限定，其中每个组件由其上边界模型替换，并且从下到上由不同网络的容量限定，其中每个组件由其下边界模型替换。研究人员正在为广播、多址接入和干扰信道等单个传输设备以及从组件模型构建的子网络开发模型。 子网络建模允许分层分析网络，产生一种扩展到非常大的网络的技术。

----Communication networks are, increasingly, the backbone upon which the world's financial, educational, and governmental institutions rely. Surprisingly, very little is known about how much information current communication networks can carry. The absence of tools for characterizing the "capacities" of large, complex networks makes it difficult to determine how to improve the capabilities of current communication networks or to design better networks for the future. This research involves the development of new systematic strategies for building computational tools for characterizing the capacities of large networks.This research involves bounding the capacity of a complex network by first factoring the network into individual components, then replacing those components by simpler models, and finally employing computational tools to bound the capacities of the modeling networks. Researchers create a library of component models by deriving both upper and lower bounding models for each component studied. Unlike prior mechanisms for characterizing the behaviors of individual channels, these upper and lower bounding models capture the full range of behaviors of an individual component. Thus the capacity of any network is bounded from above by the capacity of another network where each component is replaced by its upper bounding model and bounded from below by the capacity of a distinct network where each component is replaced by its lower bounding model. Researchers are developing models for both individual transmission devices like broadcast, multiple access, and interference channels and sub-networks built from component models. Modeling sub-networks allows networks to be analyzed hierarchically, yielding a technique that scales to very large networks.