Comparative study of Kpower means, ant colony optimization, kernel power density-based estimation, and Gaussian mixture model for wireless propagation multipath clustering

• Main Authors: Teologo, Antipas, Materum, Lawrence, Blanza, Jojo, Hirano, Takuichi
• Format: text
• Published: Animo Repository 2020
• Subjects: Ant algorithms; Gaussian distribution; Radio frequency; Electrical and Electronics
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1762; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2761/type/native/viewcontent
• Institution: De La Salle University

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 KPower 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, 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. © 2020, World Academy of Research in Science and Engineering. All rights reserved.