Cluster-wise jaccard accuracy of kpower means on multipath datasets
This paper presents the accuracy performance of the KPower Means (KPM) algorithm in clustering wireless multipaths using the generated datasets from COST2100 channel model (C2CM). KPM is one of the popular techniques used to cluster wireless multipath components (MPCs) and has been a basis of other...
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| Main Authors: | , |
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| Format: | text |
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Animo Repository
2019
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| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/2589 |
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| Institution: | De La Salle University |
| Summary: | This paper presents the accuracy performance of the KPower Means (KPM) algorithm in clustering wireless multipaths using the generated datasets from COST2100 channel model (C2CM). KPM is one of the popular techniques used to cluster wireless multipath components (MPCs) and has been a basis of other complex multipath clustering approaches. KPM is implemented in Matlab using eight different channel scenarios obtained from C2CM representing various indoor and semi-urban environments at 2.85 MHz and 5.3 GHz bands, respectively. Results show that KPM performs well in an indoor environment than in a semi-urban due to the presence of numerous scatterers in a semi-urban environment yielding more multipaths. Jaccard similarity index is used to validate the accuracy performance of the KPM. © 2019, World Academy of Research in Science and Engineering. All rights reserved. |
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