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Cited 5 time in webofscience Cited 6 time in scopus
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A Group-based Channel Assignment Protocol for Rate Separation in IEEE 802.11-based Multi-Radio Multi-Rate Ad Hoc Networks SCIE SCOPUS

Title
A Group-based Channel Assignment Protocol for Rate Separation in IEEE 802.11-based Multi-Radio Multi-Rate Ad Hoc Networks
Authors
Sok Hyong KimDong Wook kimSuh, YJ
Date Issued
2012-01
Publisher
ELSEVIER SCIENCE BV
Abstract
Today's IEEE 802.11 devices support multiple channels and data rates. Utilizing multiple channels and data rates can increase the performance of IEEE 802.11 networks. However, the multi-channel design to exploit available channels is one of the challenging issues. Moreover, performance anomaly occurs in IEEE 802.11 multi-rate networks when high-rate and low-rate links share a common channel, which degrades the overall network capacity significantly. In this paper, we introduce an extension of conflict graph, called rate conflict graph (RCG), to understand the performance anomaly problem in IEEE 802.11 multi-rate networks. Then, we propose a group-based channel assignment (GCA) protocol for IEEE 802.11-based multi-radio multi-rate single-hop ad hoc networks. In GCA, each node is equipped with multiple IEEE 802.11 interfaces, and links are subdivided into multiple groups, called component groups, by obeying the interface constraints. Then, GCA utilizes RCG and a heuristic algorithm to separate different data rate links via multiple channels so that the performance anomaly problem is addressed. Our extensive simulation results reveal that GCA achieves improved performance over existing channel assignment protocols designed to consider performance anomaly. (C) 2011 Elsevier B.V. All rights reserved.
Keywords
Ad hoc network; IEEE 802.11; Multi-channel; Multi-radio; Multi-rate; WIRELESS MESH NETWORKS; PERFORMANCE; WLANS
URI
https://oasis.postech.ac.kr/handle/2014.oak/17312
DOI
10.1016/J.ADHOC.2011.06.001
ISSN
1570-8705
Article Type
Article
Citation
AD HOC NETWORKS, vol. 10, no. 1, page. 95 - 110, 2012-01
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서영주SUH, YOUNG JOO
Grad. School of AI
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