An Improved K-Power Means Technique Using Minkowski Distance Metric and Dimension Weights for Clustering Wireless Multipaths in Indoor Channel Scenarios

Abstract

Wireless multipath clustering is an important area in channel modeling, and an accurate channel model can lead to a reliable wireless environment. Finding the best technique in clustering wireless multipath is still challenging due to the radio channels’ time-variant characteristics. Several clustering techniques have been developed that offer an improved performance but only consider one or two parameters of the multipath components. This study improved the K-PowerMeans technique by incorporating weights or loads based on the principal component analysis and utilizing the Minkowski distance metric to replace the Euclidean distance. K-PowerMeans is one of the several methods in clustering wireless propagation multipaths and has been widely studied. This improved clustering technique was applied to the indoor datasets generated from the COST 2100 channel Model and considered the multipath components’ angular domains and their delay. The Jaccard index was used to determine the new method’s accuracy performance. The results showed a significant improvement in the clustering of the developed algorithm than the standard K-PowerMeans.

Energy Effective Heterogeneous Grouping Ideal Transmission System With Fuzzy Identification in Uwsntion in Uwsn

“…When applied to the indoor data sets produced by the COST 2100 channel Model, his improved grouping algorithm took into account the precise areas and their deferral of the multi-path sections. To determine how precisely the new technique should be employed, the Jaccard file was used [21]. [22] Another type of two-way EH hand-off beam forming framework with two handsets and numerous single-radio wire transfers is planned.…”