ABSTRACT
Asynchronous Transfer Mode (ATM) Broadband networks support a wide range of multimedia traffic. Accurate characterization of the multimedia traffic is essential, in ATM networks, in order to develop a robust set of traffic descriptors. Such set is required, by the Usage Parameter Control (UPC) algorithm, for traffic enforcement. It is, also, required, by the Call Admission Control (CAC) algorithm for bandwidth allocation utilizing the statistical multiplexing gain. In this paper, we present a novel approach to characterize and model the multimedia traffic using Neural Networks (NNs). A backpropagation neural network is used to characterize and predict the statistical variations of the packet arrival process resulting from the superposition of N packetized video sources and M packetized voice sources. The accuracy of the results were verified by matching the Index of Dispersion for Counts (IDC), the variance, and the autocorrelation of the arrival process to those of the NN output. The results show that the NNs can be successfully utilized to characterize the complex nonrenewal process. Hence, NNs have an excellent potential as traffic descriptors for the (UPC) and the (CAC) algorithms.
