### Abstract

A novel combinatorial optimization algorithm called "Gradual neural network (GNN)" is presented for NPcomplete broadcast scheduling problems in packet radio (PR) networks. A PR network provides data communications services to a set of geographically distributed nodes through a common radio channel. A time division multiple access (TDMA) protocol is adopted for conflict-free communications, where packets are transmitted in repetition of fixed-length time-slots called a TDMA cycle. Given a PR network, the goal of GNN is to find a TDMA cycle with the minimum delay time for each node to broadcast packets. GNN for the JV-node-M-slot TDMA cycle problem consists of a neural network with N x M binary neurons and a gradual expansion scheme. The neural network not only satisfies the constraints but also maximizes transmissions by two energy functions, whereas the gradual expansion scheme minimizes the cycle length by gradually expanding the size of the neural network. The performance is evaluated through extensive simulations in benchmark instances and in geometric graph instances with up to 1000 vertices, where GNN always finds better TDMA cycles than existing algorithms. The result in this paper supports the credibility of our GNN algorithm for a class of combinatorial optimization problems.

Original language | English |
---|---|

Pages (from-to) | 815-823 |

Number of pages | 9 |

Journal | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences |

Volume | E82-A |

Issue number | 5 |

Publication status | Published - 1999 |

Externally published | Yes |

### Fingerprint

### Keywords

- Broadcast scheduling problem
- Neural network
- Packet radio network
- Tdma cycle

### ASJC Scopus subject areas

- Electrical and Electronic Engineering
- Hardware and Architecture
- Information Systems

### Cite this

*IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences*,

*E82-A*(5), 815-823.

**A gradual neural network algorithm for broadcast scheduling problems in packet radio networks.** / Funabiki, Nobuo; Kitamichi, Junji.

Research output: Contribution to journal › Article

*IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences*, vol. E82-A, no. 5, pp. 815-823.

}

TY - JOUR

T1 - A gradual neural network algorithm for broadcast scheduling problems in packet radio networks

AU - Funabiki, Nobuo

AU - Kitamichi, Junji

PY - 1999

Y1 - 1999

N2 - A novel combinatorial optimization algorithm called "Gradual neural network (GNN)" is presented for NPcomplete broadcast scheduling problems in packet radio (PR) networks. A PR network provides data communications services to a set of geographically distributed nodes through a common radio channel. A time division multiple access (TDMA) protocol is adopted for conflict-free communications, where packets are transmitted in repetition of fixed-length time-slots called a TDMA cycle. Given a PR network, the goal of GNN is to find a TDMA cycle with the minimum delay time for each node to broadcast packets. GNN for the JV-node-M-slot TDMA cycle problem consists of a neural network with N x M binary neurons and a gradual expansion scheme. The neural network not only satisfies the constraints but also maximizes transmissions by two energy functions, whereas the gradual expansion scheme minimizes the cycle length by gradually expanding the size of the neural network. The performance is evaluated through extensive simulations in benchmark instances and in geometric graph instances with up to 1000 vertices, where GNN always finds better TDMA cycles than existing algorithms. The result in this paper supports the credibility of our GNN algorithm for a class of combinatorial optimization problems.

AB - A novel combinatorial optimization algorithm called "Gradual neural network (GNN)" is presented for NPcomplete broadcast scheduling problems in packet radio (PR) networks. A PR network provides data communications services to a set of geographically distributed nodes through a common radio channel. A time division multiple access (TDMA) protocol is adopted for conflict-free communications, where packets are transmitted in repetition of fixed-length time-slots called a TDMA cycle. Given a PR network, the goal of GNN is to find a TDMA cycle with the minimum delay time for each node to broadcast packets. GNN for the JV-node-M-slot TDMA cycle problem consists of a neural network with N x M binary neurons and a gradual expansion scheme. The neural network not only satisfies the constraints but also maximizes transmissions by two energy functions, whereas the gradual expansion scheme minimizes the cycle length by gradually expanding the size of the neural network. The performance is evaluated through extensive simulations in benchmark instances and in geometric graph instances with up to 1000 vertices, where GNN always finds better TDMA cycles than existing algorithms. The result in this paper supports the credibility of our GNN algorithm for a class of combinatorial optimization problems.

KW - Broadcast scheduling problem

KW - Neural network

KW - Packet radio network

KW - Tdma cycle

UR - http://www.scopus.com/inward/record.url?scp=0032645108&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032645108&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0032645108

VL - E82-A

SP - 815

EP - 823

JO - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

JF - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

SN - 0916-8508

IS - 5

ER -