Wireless Propagation Multipath Clustering: On Simultaneously Solving the Membership and the Number of Clusters

Blanza, Jojo F.

doi:10.30534/ijatcse/2019/15852019

Cited by 5 publications

(3 citation statements)

References 10 publications

(12 reference statements)

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“…Table 1 shows the Jaccard indices of the eight-channel scenarios. The results are the completion of the previous works [14], [15]. Indoor channel scenarios have better accuracy both for the number of clusters and membership of clusters due to lesser number of multipaths and multipath clusters generated by the enclosed space where reflections of signals are limited.…”

Section: Resultssupporting

confidence: 65%

Joint Identification of the Clustering and Cardinality of Wireless Propagation Multipaths

Blanza¹

2019

IJETER

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COST 2100 channel model (C2CM) wireless propagation multipaths taken from IEEE DataPort are grouped using simultaneous clustering and model selection (SCAMS). SCAMS solves simultaneously the membership and the number of clusters in contrast to just the number of clusters popular clustering approaches provide. The membership and the number of clusters rely on and , the parameters that avoid the trivial solution of the affinity matrix. Jaccard index is used to assess the accuracy of SCAMS by comparing the clustered multipaths with the reference multipath datasets from IEEE DataPort.

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Section: Resultssupporting

confidence: 65%

Joint Identification of the Clustering and Cardinality of Wireless Propagation Multipaths

Blanza¹

2019

IJETER

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“…It is depicted as a normal measure of minute between the start of the scattering of an information and the landing of data on the hub that will be keen on getting that data. The time effectiveness for each message is in this way determined by idleness [14].…”

Section: Latencymentioning

confidence: 99%

Threshold-Sensitive Energy Efficient Network and Low Energy Adaptive Clustering Hierarchy Protocols’ Performance Appraisal in Wireless Sensor Networks

2020

IJATCSE

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Due to the inability of being frequently charged the sensor nodes in the WSN in non-accessible milieu is the main concern of enhancing battery lifetime. And for its WSN protocols play a pivotal role. Effectiveness of wireless sensor networks (WSN) varies on routing protocols. The WSN comprise of an enormous number of little nodes that procedure, store or transfer information inside constrained range on account of their little size nodes. Installed sensors are broadly utilized in an alternate sort of uses, for example, tracking or health checking. Information sent with a particular measure of normal start to finish delay, throughput, and jitter in these systems. Multiple protocols are used to overcome that con. Routing helps the (WSN) to pave the way towards better usage of limited powered batteries. The humongous evolvement in the sensor network had been made due to enhancement in the demand of niche solutions for the limited and scarce energy support in WSN. Hierarchical routing is one of the paramount routing techniques which dells with the alike dissemination of burden in network, foremost to the lifetime enchantment of the network. Performance of Threshold-sensitive Energy Efficient Network (TEEN) protocol and Low Energy Adaptive Clustering Hierarchy (LEACH) protocols will be paralleled. This study will be examinee a fore mentioned protocols on the basic of entire energy consumed and sensors' lifespan and make available the appraisal of TEEN and LEACH with imitation established results. Mainly the futures like energy, node alive, nodes data and period will compare. CBR and Video traffic will be used in WSN to compare these features. The aim of aforementioned routing protocols is to achieve a more lengthy actuality of nodes. Another aim of this research is to create the communication amongst nodes and the Base Station (BS) cost-effectively. For simulation, a tool NS-II will be used. The conclusions of this research will be worthwhile for various purposes either non-domestic or domestic.

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“…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

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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.

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Wireless Propagation Multipath Clustering: On Simultaneously Solving the Membership and the Number of Clusters

Cited by 5 publications

References 10 publications

Joint Identification of the Clustering and Cardinality of Wireless Propagation Multipaths

Joint Identification of the Clustering and Cardinality of Wireless Propagation Multipaths

Threshold-Sensitive Energy Efficient Network and Low Energy Adaptive Clustering Hierarchy Protocols’ Performance Appraisal in Wireless Sensor Networks

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

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