A throughput drop estimation model for concurrent communications under partially overlapping channels without channel bonding and its application to channel assignment in IEEE 802.11n WLAN

Kwenga Ismael Munene, Nobuo Funabiki, Hendy Briantoro, Md Mahbubur Rahman, Fatema Akhter, Minoru Kuribayashi, Wen Chung Kao

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

SUMMARY Currently, the IEEE 802.11n wireless local-area network (WLAN) has been extensively deployed world-wide. For the efficient channel assignment to access-points (APs) from the limited number of partially overlapping channels (POCs) at 2.4GHz band, we have studied the throughput drop estimation model for concurrently communicating links using the channel bonding (CB). However, non-CB links should be used in dense WLANs, since the CB links often reduce the transmission capacity due to high interferences from other links. In this paper, we examine the throughput drop estimation model for concurrently communicating links without using the CB in 802.11n WLAN, and its application to the POC assignment to the APs. First, we verify the model accuracy through experiments in two network fields. The results show that the average error is 9.946% and 6.285% for the high and low interference case respectively. Then, we verify the effectiveness of the POC assignment to the APs using the model through simulations and experiments. The results show that the model improves the smallest throughput of a host by 22.195% and the total throughput of all the hosts by 22.196% on average in simulations for three large topologies, and the total throughput by 12.89% on average in experiments for two small topologies.

Original languageEnglish
Pages (from-to)585-596
Number of pages12
JournalIEICE Transactions on Information and Systems
VolumeE104.D
Issue number5
DOIs
Publication statusPublished - 2021

Keywords

  • Access-point
  • Concurrent communication
  • Non-bonded channel
  • Partially overlapping channel
  • Throughput drop estimation
  • WLAN

ASJC Scopus subject areas

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

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