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

Nobuo Funabiki, Junji Kitamichi

Research output: Contribution to journalArticle

19 Citations (Scopus)

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 languageEnglish
Pages (from-to)815-823
Number of pages9
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE82-A
Issue number5
Publication statusPublished - 1999
Externally publishedYes

Fingerprint

Radio Networks
Network Algorithms
Broadcast
Scheduling Problem
Scheduling
Neural Networks
Time division multiple access
Multiple Access
Neural networks
Division
Cycle
Combinatorial optimization
Vertex of a graph
Geometric Graphs
Network Communication
Cycle Length
Combinatorial Algorithms
Data Communication
Communication
Credibility

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

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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.

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