A gradual neural-network algorithm for jointly time-slot/code assignment problems in packet radio networks

Nobuo Funabiki, Junji Kitamichi

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

A gradual neural network (GNN) algorithm is presented for the jointly time-slot/code assignment problem (JTCAP) in a packet radio network in this paper. The goal of this newly defined problem is to find a simultaneous assignment of a time-slot and a code to each communication link, whereas time-slots and codes have been independently assigned in existing algorithms. A time/code division multiple access protocol is adopted for conflict-free communications, where packets are transmitted in repetition of fixed-length time-slots with specific codes. GNN seeks the time-slot/code assignment with the minimum number of time-slots subject to two constraints: 1) the number of codes must not exceed its upper limit and 2) any couple of links within conflict distance must not be assigned to the same time-slot/code pair. The restricted problem for only one code is known to be NP-complete. The performance of GNN is verified through solving 3000 instances with 100-500 nodes and 100-1000 links. The comparison with the lower bound and a greedy algorithm shows the superiority of GNN in terms of the solution quality with the comparable computation time.

Original languageEnglish
Pages (from-to)1523-1528
Number of pages6
JournalIEEE Transactions on Neural Networks
Volume9
Issue number6
DOIs
Publication statusPublished - Dec 1 1998
Externally publishedYes

Keywords

  • Code
  • Gradual neural network
  • NP-complete
  • Packet radio network
  • Time-slot

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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