An Improved K-Power Means Technique Using Minkowski Distance Metric and Dimension Weights for Clustering Wireless Multipaths in Indoor Channel Scenarios
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 cluster...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universiti Utara Malaysia Press
2021
|
| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/28761/1/JICT%2020%2004%202021%20541-563.pdf https://doi.org/10.32890/jict2021.20.4.4 |
| _version_ | 1803629516512296960 |
|---|---|
| author | Materum, Lawrence Teologo Jr, Antipas |
| author_facet | Materum, Lawrence Teologo Jr, Antipas |
| author_sort | Materum, Lawrence |
| collection | UUM |
| description | 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. |
| first_indexed | 2024-07-04T06:39:06Z |
| format | Article |
| id | uum-28761 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:39:06Z |
| publishDate | 2021 |
| publisher | Universiti Utara Malaysia Press |
| record_format | dspace |
| spelling | uum-287612023-06-19T15:22:48Z https://repo.uum.edu.my/id/eprint/28761/ An Improved K-Power Means Technique Using Minkowski Distance Metric and Dimension Weights for Clustering Wireless Multipaths in Indoor Channel Scenarios Materum, Lawrence Teologo Jr, Antipas TK Electrical engineering. Electronics Nuclear engineering 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. Universiti Utara Malaysia Press 2021 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/28761/1/JICT%2020%2004%202021%20541-563.pdf 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 https://e-journal.uum.edu.my/index.php/jict/article/view/13834 https://doi.org/10.32890/jict2021.20.4.4 https://doi.org/10.32890/jict2021.20.4.4 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Materum, Lawrence Teologo Jr, Antipas An Improved K-Power Means Technique Using Minkowski Distance Metric and Dimension Weights for Clustering Wireless Multipaths in Indoor Channel Scenarios |
| title | An Improved K-Power Means Technique Using Minkowski Distance Metric and Dimension Weights for Clustering Wireless Multipaths in Indoor Channel Scenarios |
| title_full | An Improved K-Power Means Technique Using Minkowski Distance Metric and Dimension Weights for Clustering Wireless Multipaths in Indoor Channel Scenarios |
| title_fullStr | An Improved K-Power Means Technique Using Minkowski Distance Metric and Dimension Weights for Clustering Wireless Multipaths in Indoor Channel Scenarios |
| title_full_unstemmed | An Improved K-Power Means Technique Using Minkowski Distance Metric and Dimension Weights for Clustering Wireless Multipaths in Indoor Channel Scenarios |
| title_short | An Improved K-Power Means Technique Using Minkowski Distance Metric and Dimension Weights for Clustering Wireless Multipaths in Indoor Channel Scenarios |
| title_sort | improved k power means technique using minkowski distance metric and dimension weights for clustering wireless multipaths in indoor channel scenarios |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | https://repo.uum.edu.my/id/eprint/28761/1/JICT%2020%2004%202021%20541-563.pdf https://doi.org/10.32890/jict2021.20.4.4 |
| work_keys_str_mv | AT materumlawrence animprovedkpowermeanstechniqueusingminkowskidistancemetricanddimensionweightsforclusteringwirelessmultipathsinindoorchannelscenarios AT teologojrantipas animprovedkpowermeanstechniqueusingminkowskidistancemetricanddimensionweightsforclusteringwirelessmultipathsinindoorchannelscenarios AT materumlawrence improvedkpowermeanstechniqueusingminkowskidistancemetricanddimensionweightsforclusteringwirelessmultipathsinindoorchannelscenarios AT teologojrantipas improvedkpowermeanstechniqueusingminkowskidistancemetricanddimensionweightsforclusteringwirelessmultipathsinindoorchannelscenarios |