Neural networks based on adjustable-order statistic filters for multimedia multicast routing

Abstract

Multicast routing in communication networks is to transmit information from a single source to multiple destinations, using the network resources very effectively, and respecting several constraints, such as delay, cost, bandwidth or other. To guarantee optimal diffusion, it is necessary to determine a tree that connects the source node to all destination nodes minimizing the use of resources. In this paper, we propose an artificial neural network for the construction of the multicast tree, based on adjustable-order statistic filters. Our approach for solving this problem differs from the conventional approach used in the field of neural networks. Our primary concern is how to organize neurons into a network so that it can solve a specific problem, with an emphasis on fully utilizing the massive parallelism property offered by neural networks.