Parameter tuning of the protocol interference model using SINR for time slot assignment inwireless mesh networks

Gyeongyeon Kang, Yoshiaki Taniguchi, Go Hasegawa, Hirotaka Nakano

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

In time division multiple access (TDMA)-based wireless mesh networks, interference relationships should be considered when time slots are assigned to links. In graph theory-based time slot assignment algorithms, the protocol interference model is widely used to determine radio interference information, although it is an inaccurate model of actual radio interference. On the other hand, the signal-to-interference-plus-noise-ratio model (SINR model) gives more accurate interference relationships but is difficult to apply to time slot assignment algorithms since the radio interference information cannot be determined before time slot assignment. In this paper, we investigate the effect of the parameters of the protocol interference model on the accuracy of the interference relationships determined using this model. Specifically, after assigning time slots to links based on the protocol interference model with various interference ratios, which is the major parameter of the protocol interference model, we compare the interference relationship among links in the protocol interference and SINR models. Through simulation experiments, we show that accuracy of the protocol interference model is improved by up to 15% by adjusting the interference ratios of the protocol interference model.

Original languageEnglish
Pages (from-to)2191-2200
Number of pages10
JournalIEICE Transactions on Information and Systems
VolumeE94-D
Issue number11
DOIs
Publication statusPublished - 2011 Nov
Externally publishedYes

Keywords

  • Parameter tuning
  • Protocol interference model
  • SINR model
  • TDMA
  • Wireless mesh networks

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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