### 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 language | English |
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Title of host publication | 91 IEEE Int Jt Conf Neural Networks IJCNN 91 |

Publisher | Publ by IEEE |

Pages | 2540-2545 |

Number of pages | 6 |

ISBN (Print) | 0780302273 |

Publication status | Published - 1991 |

Externally published | Yes |

Event | 1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 - Singapore, Singapore Duration: Nov 18 1991 → Nov 21 1991 |

### Other

Other | 1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 |
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City | Singapore, Singapore |

Period | 11/18/91 → 11/21/91 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*91 IEEE Int Jt Conf Neural Networks IJCNN 91*(pp. 2540-2545). Publ by IEEE.

**A neural network approach to broadcasting in multihop packet radio networks.** / Funabiki, Nobuo; Takefuji, Yoshiyasu; Lee, Kuo Chun; Cho, Yong Beom; Kurokawa, Takakazu; Aiso, Hideo.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*91 IEEE Int Jt Conf Neural Networks IJCNN 91.*Publ by IEEE, pp. 2540-2545, 1991 IEEE International Joint Conference on Neural Networks - IJCNN '91, Singapore, Singapore, 11/18/91.

}

TY - GEN

T1 - A neural network approach to broadcasting in multihop packet radio networks

AU - Funabiki, Nobuo

AU - Takefuji, Yoshiyasu

AU - Lee, Kuo Chun

AU - Cho, Yong Beom

AU - Kurokawa, Takakazu

AU - Aiso, Hideo

PY - 1991

Y1 - 1991

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0026264271&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0026264271&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0026264271

SN - 0780302273

SP - 2540

EP - 2545

BT - 91 IEEE Int Jt Conf Neural Networks IJCNN 91

PB - Publ by IEEE

ER -