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: | , , |
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| Format: | text |
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Animo Repository
2020
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| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/2592 |
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| Institution: | De La Salle University |
| Summary: | 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. |
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