A GRADUAL NEURAL-NETWORK APPROACH FOR TIME-SLOT ASSIGNMENT IN TDM MULTICAST SWITCHING SYSTEMS

A neural network approach called the ''Gradual Neural Network (GNN)'' for the time slot assignment problem in the TDM multicast switching sy stem is presented in this paper. The goal of this NP-complete problem is to find an assignment of packet transmission requests into a minimu m number of time slots. A packet can be transmitted from one source to several destinations simultaneously by its replication. A time slot r epresents a switching configuration of the system with unit time for e ach packer transmission through an I/O line. The GNN consists of the b inary neural network and the gradual expansion scheme. The binary neur al network satisfies the constraints imposed on the system by solving the motion equation, whereas the gradual expansion scheme minimizes th e number of required time slots by gradually expanding activated neuro ns. The performance is evaluated through simulations in practical size systems, where the GNN finds far better solutions than the best exist ing algorithm.