Deep Divergence-Based Clustering of Wireless Multipaths for Simultaneously Addressing the Grouping and the Cardinality

Blanza, Jojo F.

doi:10.30534/ijeter/2020/37872020

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…Factorization, clustering accuracy, computational complexity and robustness were the areas of evaluation. The clustering performance of SC and 3CAM-SC was compared with that of SCAMSMA and DDC (6,5).…”

Section: Resultsmentioning

confidence: 99%

“…SCAMSMA represents the data as the product of the data and an affinity matrix to solve the number and membership of multipath clusters simultaneously. Blanza, Materum, and Hirano (2020) applied deep divergence-based clustering to solve the membership of the multipath clusters (5,15). The cluster count was then calculated according to the membership of the multipaths to their clusters.…”

Section: Figure 2 Multipath Clusters In Mobile Wireless Communications (27)mentioning

confidence: 99%

“…Clustering of wireless propagation multipaths gained interest due to the widespread application of multiple-input multiple-output (MIMO) antennas in wireless communications systems (5,6). MIMO systems are developed to increase data rates and ensure wireless transmission reliability (7).…”

Section: Introductionmentioning

confidence: 99%

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Wireless Propagation Multipaths using Spectral Clustering and Three-Constraint Affinity Matrix Spectral Clustering

Blanza

2021

Baghdad Sci.J

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This study focused on spectral clustering (SC) and three-constraint affinity matrix spectral clustering (3CAM-SC) to determine the number of clusters and the membership of the clusters of the COST 2100 channel model (C2CM) multipath dataset simultaneously. Various multipath clustering approaches solve only the number of clusters without taking into consideration the membership of clusters. The problem of giving only the number of clusters is that there is no assurance that the membership of the multipath clusters is accurate even though the number of clusters is correct. SC and 3CAM-SC aimed to solve this problem by determining the membership of the clusters. The cluster and the cluster count were then computed through the cluster-wise Jaccard index of the membership of the multipaths to their clusters. The multipaths generated by C2CM were transformed using the directional cosine transform (DCT) and the whitening transform (WT). The transformed dataset was clustered using SC and 3CAM-SC. The clustering performance was validated using the Jaccard index by comparing the reference multipath dataset with the calculated multipath clusters. The results show that the effectiveness of SC is similar to the state-of-the-art clustering approaches. However, 3CAM-SC outperforms SC in all channel scenarios. SC can be used in indoor scenarios based on accuracy, while 3CAM-SC is applicable in indoor and semi-urban scenarios. Thus, the clustering approaches can be applied as alternative clustering techniques in the field of channel modeling.

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Section: Resultsmentioning

confidence: 99%

Section: Figure 2 Multipath Clusters In Mobile Wireless Communications (27)mentioning

confidence: 99%

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Wireless Propagation Multipaths using Spectral Clustering and Three-Constraint Affinity Matrix Spectral Clustering

Blanza

2021

Baghdad Sci.J

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“…It uses divergence to assess the separation between clusters and compactness within clusters by comparing the dissimilarity between clusters based on their probability density functions. The innovative divergence-based loss function for clustering allowed end-to-end unsupervised learning by optimizing the geometry of the output space [9]. K-Power Means (KPM) is where the number of cluster centroids is iteratively adjusted around the data space to reduce the multipath component distance between MPCs and their closest centroid, based on the k-means algorithm.…”

Section: Existing Multipath Clustering Approachmentioning

confidence: 99%

Indoor Wireless Multipaths Outlier Detection and Clustering

Blanza

Cabasal²,

Cipriano³

et al. 2022

J. Phys.: Conf. Ser.

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Wireless communication systems have grown and developed significantly in recent years to fulfill the growing demand for high data rates across a wireless medium. Channel models have been used to develop various sturdy wireless systems for indoor and outdoor applications, and these are simulated in the form of datasets. The presence of outliers in clusters has been a concern in datasets, as it affects the standard deviation and mean of the dataset which reduces the data accuracy. In this study, the outliers in the Cooperation in Science and Technology (COST) 2100 MIMO channel model dataset were shifted to the means of the clusters using the Mean Shift Outlier Detection method. Afterward, the data is clustered using simultaneous clustering and model selection matrix affinity (SCAMSMA). The Mean Shift Outlier Detection method identified 52 and 46 multipaths as outliers and improved the clustering accuracy of the indoor scenarios by 3.5% and 0.93%, respectively. It also increased the precision of the clustering based on the decrease in standard deviation of the Jaccard indices from 0.2435 to 0.1807 and 0.3038 to 0.2075.

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“…It also group similar data together [5]- [16]. In our previous works [17]- [20], Simultaneous Clustering and Model Selection Matrix Affinity (SCAMSMA) [21] and Divergence-Based Clustering (DDC) [22] were used to cluster the dataset [23,24] generated by C2CM. In this work, the comparison of the clustering accuracy of SCAMSMA and DDC are presented.…”

Section: Introductionmentioning

confidence: 99%

Variation Between DDC and SCAMSMA for Clustering of Wireless MultipathWaves in Indoor and Semi-Urban Channel Scenarios

Blanza¹,

Materum²

2020

Adv. sci. technol. eng. syst. j.

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The performance of Simultaneous Clustering and Model Selection Matrix Affinity (SCAMSMA) and Deep Divergence-Based Clustering (DDC) in clustering wireless multipaths generated by COST 2100 channel model (C2CM) is compared. Enhancing the accuracy of clustering multipaths is an open area of research which the clustering approaches try to improve. Jaccard index is used as the clustering validity metric of the clustering approaches. The results of the clustering approaches are compared using the analysis of variance (ANOVA) toolbox of MATLAB and displayed using the box plots. Results show that the cluster-wise Jaccard index is different between SCAMSMA and DDC for indoor scenarios, while the membership-wise Jaccard index is not. On the other hand, the cluster-wise Jaccard index is not different between the clustering approaches for semi-urban scenarios, while the membership-wise Jaccard index is a little different. The clustering approaches can be used in indoor scenarios based on accuracy.

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Deep Divergence-Based Clustering of Wireless Multipaths for Simultaneously Addressing the Grouping and the Cardinality

Cited by 3 publications

References 7 publications

Wireless Propagation Multipaths using Spectral Clustering and Three-Constraint Affinity Matrix Spectral Clustering

Wireless Propagation Multipaths using Spectral Clustering and Three-Constraint Affinity Matrix Spectral Clustering

Indoor Wireless Multipaths Outlier Detection and Clustering

Variation Between DDC and SCAMSMA for Clustering of Wireless MultipathWaves in Indoor and Semi-Urban Channel Scenarios

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