Identification of wireless propagation multipath clusters and their cardinality using simultaneous clustering and model selection matrix affinity and deep divergence-based clustering approaches

• Main Author: Blanza, Jojo F.
• Format: text
• Language: English
• Published: Animo Repository 2020
• Subjects: Wireless communication systems; Agricultural innovations; Electrical and Computer Engineering; Electrical and Electronics
• Online Access: https://animorepository.dlsu.edu.ph/etd_doctoral/1414; https://animorepository.dlsu.edu.ph/context/etd_doctoral/article/2469/viewcontent/Blanza_Jojo_11082526_1_NEW_Redacted.pdf
• Institution: De La Salle University

Propagation channel modeling is essential in the design, simulation, and planning of wire- less communications systems. The performance of the wireless systems can be tested even before the construction of the communications network. Many propagation channel mea- surements show that multipath components are distributed as clusters. Existing clustering approaches have low accuracy and give only the number of clusters without considering the membership of the clusters. In this paper, the results of Simultaneous Clustering and Model Selection Matrix Affinity (SCAMSMA), Deep Divergence-based Clustering (DDC), and Modified SCAMSMA in clustering multipaths of eight-channel scenarios generated by the COST 2100 channel are presented. The clustering approaches group the COST 2100 datasets by determining simultaneously the number of clusters and the membership of the clusters. SCAMSMA and DDC cluster multipaths in indoor scenarios decently but they give low accuracy, as evidenced by the Jaccard indices, in semi-urban scenarios. Modified SCAMSMA improved the clustering accuracy in all channel scenarios, hence, the new clustering approach is better suited in multipath clustering.