A gradual neural network approach for time slot assignment in tdm multicast switching systems

Nobuo Funabiki, Junji Kitamichi, Seishi Nishikawa

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

A neural network approach called the "Gradual Neural Network (GNN)" for the time slot assignment problem in the TDM multicast switching system is presented in this paper. The goal of this NP-complete problem is to find an assignment of packet transmission requests into a minimum number of time slots. A packet can be transmitted from one source to several destinations simultaneously by its replication. A time slot represents a switching configuration of the system with unit time for each packet transmission through an I/O line. The GNN consists of the binary neural network and the gradual expansion scheme. The binary neural network satisfies the constraints imposed on the system by solving the motion equation, whereas the gradual expansion scheme minimizes the number of required time slots by gradually expanding activated neurons. The performance is evaluated through simulations in practical size systems, where the GNN finds far better solutions than the best existing algorithm.

Original languageEnglish
Pages (from-to)939-947
Number of pages9
JournalIEICE Transactions on Communications
VolumeE80-B
Issue number6
Publication statusPublished - Jan 1 1997
Externally publishedYes

Keywords

  • Combinatorial optimization
  • Neural network
  • Tdm multicast switching system
  • Time slot assignment

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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