The performance evaluation of a new neural network-based traffic management scheme for a satellite communication network

Nirwan Ansari, Dequan Liu

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents the first attempt to incorporate the map configuration task with the routing task in the traffic management scheme of a satellite communication network. The map configuration task, which is automated by a self-organization scheme modified from Kohonen's model, consists of three stages. The first stage is the pattern recognition task, which selects an exemplar map that best meets the current network requirement. The second stage consists of analyzing the discrepancy between the chosen exemplar map and the state of the network, and adaptively modifying the chosen exemplar map to conform closely to the network requirement (input data pattern) by self-organization. Based on certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. The routing task is implemented by a state-dependent routing algorithm which arranges the incoming call to some proper path to make the network more efficient, and lowers the call block rate. The new scheme inheriting merits of both the map configuration and routing tasks provides the best compromised performance in terms of block rates at various load conditions as compared to status quo approaches - schemes with either only the map configuration task or only the routing task.

Original languageEnglish (US)
Pages (from-to)263-282
Number of pages20
JournalNeurocomputing
Volume8
Issue number3
DOIs
StatePublished - Aug 1995

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Keywords

  • Kohonen
  • Map configuration
  • Satellite communication network
  • Self-organization
  • State-dependent routing
  • Traffic management

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