Deep divergence-based clustering of wireless multipaths for simultaneously addressing the grouping and the cardinality

Deep divergence-based clustering (DDC) is used to cluster COST 2100 channel model (C2CM) wireless propagation multipaths. The dataset is taken from the IEEE DataPort. DDC solves the membership of the clusters. DDC builds on information theoretic divergence measures and geometric regularization in or...

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Main Authors:	Blanza, Jojo, Materum, Lawrence, Hirano, Takuichi
Format:	text
Published:	Animo Repository 2020
Subjects:	MIMO systems Radio wave propagation Electrical and Electronics Manufacturing Systems and Communications
Online Access:	https://animorepository.dlsu.edu.ph/faculty_research/2592
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Institution:	De La Salle University

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spelling	oai:animorepository.dlsu.edu.ph:faculty_research-35912021-10-19T00:43:24Z Deep divergence-based clustering of wireless multipaths for simultaneously addressing the grouping and the cardinality Blanza, Jojo Materum, Lawrence Hirano, Takuichi Deep divergence-based clustering (DDC) is used to cluster COST 2100 channel model (C2CM) wireless propagation multipaths. The dataset is taken from the IEEE DataPort. DDC solves the membership of the clusters. DDC builds on information theoretic divergence measures and geometric regularization in order to determine the membership of the clusters. The cluster count is then computed through the cluster-wise Jaccard index of the membership of the multipaths to their clusters. The performance of DDC is evaluated using the Jaccard index by comparing the reference multipathdatasets from IEEE DataPort with the calculated multipath clusters obtained by DDC. Results show that DDC can be used as an alternative clustering approach in the field of channel modeling. © 2020, World Academy of Research in Science and Engineering. All rights reserved. 2020-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/2592 Faculty Research Work Animo Repository MIMO systems Radio wave propagation Electrical and Electronics Manufacturing Systems and Communications
institution	De La Salle University
building	De La Salle University Library
continent	Asia
country	Philippines Philippines
content_provider	De La Salle University Library
collection	DLSU Institutional Repository
topic	MIMO systems Radio wave propagation Electrical and Electronics Manufacturing Systems and Communications
spellingShingle	MIMO systems Radio wave propagation Electrical and Electronics Manufacturing Systems and Communications Blanza, Jojo Materum, Lawrence Hirano, Takuichi Deep divergence-based clustering of wireless multipaths for simultaneously addressing the grouping and the cardinality
description	Deep divergence-based clustering (DDC) is used to cluster COST 2100 channel model (C2CM) wireless propagation multipaths. The dataset is taken from the IEEE DataPort. DDC solves the membership of the clusters. DDC builds on information theoretic divergence measures and geometric regularization in order to determine the membership of the clusters. The cluster count is then computed through the cluster-wise Jaccard index of the membership of the multipaths to their clusters. The performance of DDC is evaluated using the Jaccard index by comparing the reference multipathdatasets from IEEE DataPort with the calculated multipath clusters obtained by DDC. Results show that DDC can be used as an alternative clustering approach in the field of channel modeling. © 2020, World Academy of Research in Science and Engineering. All rights reserved.
format	text
author	Blanza, Jojo Materum, Lawrence Hirano, Takuichi
author_facet	Blanza, Jojo Materum, Lawrence Hirano, Takuichi
author_sort	Blanza, Jojo
title	Deep divergence-based clustering of wireless multipaths for simultaneously addressing the grouping and the cardinality
title_short	Deep divergence-based clustering of wireless multipaths for simultaneously addressing the grouping and the cardinality
title_full	Deep divergence-based clustering of wireless multipaths for simultaneously addressing the grouping and the cardinality
title_fullStr	Deep divergence-based clustering of wireless multipaths for simultaneously addressing the grouping and the cardinality
title_full_unstemmed	Deep divergence-based clustering of wireless multipaths for simultaneously addressing the grouping and the cardinality
title_sort	deep divergence-based clustering of wireless multipaths for simultaneously addressing the grouping and the cardinality
publisher	Animo Repository
publishDate	2020
url	https://animorepository.dlsu.edu.ph/faculty_research/2592
_version_	1715215542593257472

Deep divergence-based clustering of wireless multipaths for simultaneously addressing the grouping and the cardinality

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