This paper presents the accuracy performance of the KPower Means (KPM) algorithm in clustering wireless multipaths using the generated datasets from COST2100 channel model (C2CM). KPM is one of the popular techniques used to cluster wireless multipath components (MPCs) and has been a basis of other complex multipath clustering approaches. KPM is implemented in Matlab using eight different channel scenarios obtained from C2CM representing various indoor and semi-urban environments at 2.85 MHz and 5.3 GHz bands, respectively. Results show that KPM performs well in an indoor environment than in a semi-urban due to the presence of numerous scatterers in a semi-urban environment yielding more multipaths. Jaccard similarity index is used to validate the accuracy performance of the KPM.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.