Fifth Generation Wireless Channels Outlier Detection and Clustering

The fifth generation (5G) wireless communications system offers faster data rates, lower latency, and higher number of interconnecting devices. Various 5G channel models were developed to study its stochastic characteristics prior to its implementation. These channel models generate multipath components that are grouped into clusters when they have similar properties in delay and angles. The multipaths and multipath clusters are used as datasets in multipath clustering which is used to examine the propagation properties of the 5G system. However, datasets are prone to outliers. They tend to affect clustering accuracy. Hence, this study clusters the datasets generated by the channel models, remove the outliers, and cluster again the datasets free of outliers. Outlier detection shows 5G channel model datasets contain noise and outlier removal improves the modelling characteristics shown by improved clustering accuracy. Results show that most of the outliers are detected in the 2*SD theshold. The removal of the outliers increased the clustering accuracy. This shows that outlier detection and removal also work well with channel model datasets and can be used in analyzing the propagation characteristics of 5G.