A neural network approach to broadcasting in multihop packet radio networks

Nobuo Funabiki, Yoshiyasu Takefuji, Kuo Chun Lee, Yong Beom Cho, Takakazu Kurokawa, Hideo Aiso

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A neural network model for broadcasting scheduling in multihop packet radio networks is presented. The problem of broadcast scheduling with a minimum number of time slots is NP-complete. The proposed neural network model finds a broadcasting schedule with a minimal number of time slots where it requires n processing elements for an n-node radio network. Fifteen different radio networks were examined where the neural network model found an m-time-slot solution in O(m) time with n processors.

Original languageEnglish
Title of host publication91 IEEE Int Jt Conf Neural Networks IJCNN 91
PublisherPubl by IEEE
Pages2540-2545
Number of pages6
ISBN (Print)0780302273
Publication statusPublished - 1991
Externally publishedYes
Event1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 - Singapore, Singapore
Duration: Nov 18 1991Nov 21 1991

Publication series

Name91 IEEE Int Jt Conf Neural Networks IJCNN 91

Other

Other1991 IEEE International Joint Conference on Neural Networks - IJCNN '91
CitySingapore, Singapore
Period11/18/9111/21/91

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Funabiki, N., Takefuji, Y., Lee, K. C., Cho, Y. B., Kurokawa, T., & Aiso, H. (1991). A neural network approach to broadcasting in multihop packet radio networks. In 91 IEEE Int Jt Conf Neural Networks IJCNN 91 (pp. 2540-2545). (91 IEEE Int Jt Conf Neural Networks IJCNN 91). Publ by IEEE.