Interface for the Factor-inclusion Weighting Approach in Determining the Number of Multipath Propagation Clusters

Roque, Marielle Anne; Materum, Lawrence

doi:10.30534/ijatcse/2019/107842019

Cited by 3 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One challenge of clustering is also to find the number of optimal clusters. Moreover, a graphical user interface (GUI) was developed in [15] to determine cluster counts by a factor weighing approach, and it was reported that the delay and angular data were effective weight factors in the indoor and semi-urban scenarios, respectively. The non-stationary and spherical waveform was considered parameters in [16].…”

Section: Multipath Clusteringmentioning

confidence: 99%

Projection of Wireless Multipath Clusters Using Multi-Dimensional Visualization Techniques

Africa¹,

Trinidad²,

Materum³

2020

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

View full text Add to dashboard Cite

Section: Multipath Clusteringmentioning

confidence: 99%

Projection of Wireless Multipath Clusters Using Multi-Dimensional Visualization Techniques

Africa¹,

Trinidad²,

Materum³

2020

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

View full text Add to dashboard Cite

“…Over the past two decades, clustering of MPCs [1]- [2] gained much attention in the research community and is now the basis of various channel models nowadays such as the European Cooperation in Science and Technology (COST) 259, COST 2100, 3GPP Spatial Channel Model, and the European Wireless World Initiative New Radio (WINNER) due to the correspondence of the majority of the signals apart from the line-of-sight to form as significant clusters of signal energy. Various parameters of MPCs such as the angular spreads, delay, number, and position are used in the clustered structure modeling to group MPCs into clusters.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of KPower Means, Ant Colony Optimization, Kernel Power Density-based Estimation, and Gaussian Mixture Model for Wireless Propagation Multipath Clustering

Teologo¹

2020

IJETER

View full text Add to dashboard Cite

In radio communications, channel modeling has a very significant impact. Most of the system's performance relies on the behavior and characteristics of a radio channel. Due to its mobility features, radio channels are time-variant that change over time, making channel characterizations to be dynamic and very challenging. To ensure maximum data rate and reliable communication, accurate channel models are necessary, which requires a correct grouping of wireless multipaths. Accurate clustering of wireless multipath components (MPCs) is essential for cluster-based channel modeling resulting in a more reliable wireless channel system. Currently, determining the best clustering technique is still a challenge as there is no standard way of evaluating and comparing the performance of the various clustering algorithms. This work presents the comparative study on the accuracy performance of the four clustering algorithms namely K-Power Means (KPM), Ant Colony Optimization (ACO), Kernel Power Density-based Estimation (KPD), and Gaussian Mixture Model (GMM) in grouping the wireless MPCs using datasets generated from COST 2100 channel model (C2CM) which represent different Indoor and Semi-urban channel scenarios. Using the Jaccard index, an evaluation metric that compares the calculated data with the reference data, the accuracy of each algorithm is determined as well as their corresponding computational duration. A comparison of their performance is presented, and results show that KPM outperforms other clustering techniques in all channel scenarios of Indoor and Semi-urban environments, making it a right candidate for further improvements to develop a more accurate and computationally efficient clustering technique for wireless propagation multipaths.

show abstract

“…The interface is an effective method in visualizing the COST 2100 dataset and the clustering results. A previous work [17] on GUI can visually analyze the reference data and the calculated clusters but considered only the cluster count. In this work, the GUI can be used by the user to visualize, manipulate, and analyze the multipaths generated by the COST 2100 channel model (C2CM) and the clustering results of MSCAMSMA not only giving the accuracy on the number of clusters but also the membership of clusters.…”

Section: Introductionmentioning

confidence: 99%

Interface for Visualization of Wireless Propagation Multipath Clustering Outcomes

Blanza¹,

Materum²

2020

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

View full text Add to dashboard Cite

A graphical user interface (GUI) is presented to visualize the multipaths generated by COST 2100 channel model (C2CM) and the results of clustering the wireless propagation multipaths using Modified Simultaneous Clustering and Model Selection (MSCAMSMA). The usual practice of authors is to show their data and results using figures, tables, and graphs which are already sufficient to present their studies. However, the manner of displaying the data and results is static; that is, the user cannot examine them to analyze further the relationship of the different variables involved in the study. The paper presents a step further by showing dynamically the data and results which the user can manipulate according to its needs.

show abstract

Interface for the Factor-inclusion Weighting Approach in Determining the Number of Multipath Propagation Clusters

Cited by 3 publications

References 25 publications

Projection of Wireless Multipath Clusters Using Multi-Dimensional Visualization Techniques

Projection of Wireless Multipath Clusters Using Multi-Dimensional Visualization Techniques

Comparative Study of KPower Means, Ant Colony Optimization, Kernel Power Density-based Estimation, and Gaussian Mixture Model for Wireless Propagation Multipath Clustering

Interface for Visualization of Wireless Propagation Multipath Clustering Outcomes

Contact Info

Product

Resources

About