Title

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

College

Gokongwei College of Engineering

Department/Unit

Manufacturing Engineering and Management

Document Type

Article

Source Title

International Journal of Emerging Trends in Engineering Research

Volume

8

Issue

7

First Page

3104

Last Page

3110

Publication Date

1-1-2020

Abstract

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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Digitial Object Identifier (DOI)

10.30534/IJETER/2020/37872020

Disciplines

Electrical and Electronics | Manufacturing | Systems and Communications

Keywords

MIMO systems; Radio wave propagation

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