A gradual neural-network approach for frequency assignment in satellite communication systems

Nobuo Funabiki, Seishi Nishikawa

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


A novel neural-network approach called gradual neural network (GNN) is presented for a class of combinatorial optimization problems of requiring the constraint satisfaction and the goal function optimization simultaneously. The frequency assignment problem in the satellite communication system is efficiently solved by GNN as the typical problem of this class in this paper. The goal of this NP-complete problem is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate the increasing demands. The GNN consists of N × M binary neurons for the N-carrier-M-segment system with the gradual expansion scheme of activated neurons. The binary neural network achieves the constrain satisfaction with the help of heuristic methods, whereas the gradual expansion scheme seeks the cost optimization. The capability of GNN is demonstrated through solving 15 instances in practical size systems, where GNN can find far better solutions than the existing algorithm.

Original languageEnglish
Pages (from-to)1359-1370
Number of pages12
JournalIEEE Transactions on Neural Networks
Issue number6
Publication statusPublished - 1997
Externally publishedYes


  • Binary neuron
  • Combinatorial optimization
  • Frequency assignment
  • Gradual expansion scheme
  • Gradual neural network
  • NP-complete
  • Satellite communication
  • Simulation

ASJC Scopus subject areas

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


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