Maximum flow in probabilistic communication networks
Maximum flow in probabilistic communication networks
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DOI：
10.1002/cta.4490080209
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发表时间：
1980-04
影响因子：
2.3
通讯作者：
S. Nawathe;B. V. Rao
中科院分区：
文献类型：
--
作者：
S. Nawathe;B. V. Rao
本文的目的是为概率通信网络的分析和综合问题提供一种通用的方法。网络分支中的流可以是具有任意分布的相互依赖的随机变量。一种基于最佳线性预测理论的简单技术使这种网络的蒙特卡罗模拟成为可能。模拟结果表明，尽管中心极限定理不适用，但最大流量的分布(可以在网络中建立)可以用正态分布来近似。蒙特卡罗模拟结果与另一种分析方法得到的结果是一致的。本文所考虑的综合问题是寻找一个网络的支路容量，以满足给定的需求(根据它的均值和方差)，在一对感兴趣的顶点之间。直接搜索法(找出最优支路容量向量)可以同时考虑最大流的前两个矩，而不是现有的只考虑第一个矩的矩。
The purpose of this paper is to provide a general approach for the problems of analysis and synthesis of probabilistic communication networks. The flows in the branches of a network may be interdependent random variables having any arbitrary distribution. A simple technique, based on the theory of best linear prediction, enables Monte Carlo simulation of such a network. The simulation results suggest that the distribution of the maximum flow (that can be established in the network) can be approximated by a normal distribution despite the inapplicability of the central limit theorem. The results of Monte Carlo simulation are consistent with those obtained through an alternative analytical procedure. The synthesis problem considered in this paper is that of finding the branch capacities of a network so that a given demand for flow (specified in terms of its mean and variance), between a pair of vertices of interest, is satisfied. A direct search method (to find out the optimal branch capacity vector) enables simultaneous consideration of the first two moments of the maximum flow as against the existing ones which consider only the first moment.