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  5. Clustering Approach for Radio Resource Management in High Density Wireless Local Area Networks
 
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Clustering Approach for Radio Resource Management in High Density Wireless Local Area Networks

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Author(s)
ศุภฤกษ์ มานิตพรสุทธ์
Other Contributor(s)
University of the Thai Chamber of Commerce. School of Engineering
Publisher(s)
University of the Thai Chamber of Commerce
Date Issued
2013
Resource Type
Text::Report::Research report
Language
English
Abstract
From our fundamental study, we find that high density wireless LANs can introduce a high hidden node index, resulting in rapid reduction of the overall system performance. In this paper, we propose the clustering approach for radio resource management to alleviate the degradation of the overall system performance. The main objective of such algorithm is to reduce the hidden node index, therefore enhancing the performance of the system. We conduct two studies: experimental study by using wireless network test-bed and simulation study by using OPNET Modeler. The former is to measure the saturated throughput of the base system while the latter is to simulate the wireless network in high density scenarios.The results show that the proposed algorithm outperforms the traditional approach at the high degree of network density (greater than 9.3). Therefore, the proposed algorithm is a better alternative in such settings.
Sponsorship
Research Support Office, UTCC
Subject(s)
Computer and Multimedia Engineering
Journal
UTCC Engineering Research Papers
Degree Grantor
University of the Thai Chamber of Commerce
Access Rights
Open access
Rights
This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.
Rights Holder
University of the Thai Chamber of Commerce
Bibliographic Citation
Suparerk Manitpornsut (2013) Clustering Approach for Radio Resource Management in High Density Wireless Local Area Networks.
URI
https://hdl.handle.net/20.500.14437/574
Views
19
Acquisition Date
Jun 4, 2026
Downloads
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Last Month
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Acquisition Date
Jun 4, 2026
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