Indoor Wireless Multipaths Outlier Detection and Clustering

Abstract

Wireless communication systems have grown and developed significantly in recent years to fulfill the growing demand for high data rates across a wireless medium. Channel models have been used to develop various sturdy wireless systems for indoor and outdoor applications, and these are simulated in the form of datasets. The presence of outliers in clusters has been a concern in datasets, as it affects the standard deviation and mean of the dataset which reduces the data accuracy. In this study, the outliers in the Cooperation in Science and Technology (COST) 2100 MIMO channel model dataset were shifted to the means of the clusters using the Mean Shift Outlier Detection method. Afterward, the data is clustered using simultaneous clustering and model selection matrix affinity (SCAMSMA). The Mean Shift Outlier Detection method identified 52 and 46 multipaths as outliers and improved the clustering accuracy of the indoor scenarios by 3.5% and 0.93%, respectively. It also increased the precision of the clustering based on the decrease in standard deviation of the Jaccard indices from 0.2435 to 0.1807 and 0.3038 to 0.2075.