A general CAC approach using novel ant algorithm training based neural network
Shenghong Li, Zemin Liu · 2003
We propose a neural network based approach for call admission control (CAC), which is applicable to very general traffic. In our approach, a feedforward neural network is used to predict whether a new call can be accepted. The input vector of the neural network consists of a set of data reflecting the first and second-order statistical properties of the input aggregate stream, and its dimension is independent of the number of traffic classes. In addition, we give a novel ant algorithm to train the neural network. Unlike the backpropagation (BP) algorithm often used, our training algorithm can realize global optimization. Simulations show the effectiveness of our approach.