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An Improved K-Power Means Technique Using Minkowski Distance Metric and Dimension Weights for Clustering Wireless Multipaths in Indoor Channel Scenarios

Materum, Lawrence and Teologo Jr, Antipas (2021) An Improved K-Power Means Technique Using Minkowski Distance Metric and Dimension Weights for Clustering Wireless Multipaths in Indoor Channel Scenarios. Journal of Information and Communication Technology, 20 (04). pp. 541-563. ISSN 2180-3862

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Abstract

Wireless multipath clustering is an important area in channel modeling, and an accurate channel model can lead to a reliable wireless environment. Finding the best technique in clustering wireless multipath is still challenging due to the radio channels’ time-variant characteristics. Several clustering techniques have been developed that offer an improved performance but only consider one or two parameters of the multipath components. This study improved the K-PowerMeans technique by incorporating weights or loads based on the principal component analysis and utilizing the Minkowski distance metric to replace the Euclidean distance. K-PowerMeans is one of the several methods in clustering wireless propagation multipaths and has been widely studied. This improved clustering technique was applied to the indoor datasets generated from the COST 2100 channel Model and considered the multipath components’ angular domains and their delay. The Jaccard index was used to determine the new method’s accuracy performance. The results showed a significant improvement in the clustering of the developed algorithm than the standard K-PowerMeans.

Item Type: Article
Uncontrolled Keywords: Channel model, multipath clustering, Minkowski distance, principal component analysis, radio wave propagation
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: College of Arts and Sciences
Depositing User: Mrs Nurin Jazlina Hamid
Date Deposited: 31 Jul 2022 07:49
Last Modified: 19 Jun 2023 15:22
URI: https://repo.uum.edu.my/id/eprint/28761

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