# Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations
## 1 Introduction  
Research on the channel model is one of the basic works in wireless communication systems. It is an abstract description of wireless propagation characteristics. Researchers in the field of wireless communications have had great interest in channel modelling in various kinds of propagation environments (such as urban, suburb, and indoor) since the 1970s. They focused on how to build a mathematical model based on theoretical and measurement analysis, which approximately simulates real channel conditions. From the 3G to the 4G era, the channel model has already changed from the tapped delay line (TDL) model in M. 1225 [1], which is known as the International Mobile Telecommunications-2000 (IMT-2000) channel model, to geometry-based stochastic channel model (GBSM) in M. 2135 [2], which is known as IMT-Advanced channel model. ITU radiocommunication sector (ITU-R) started the project to draft a new channel model, called IMT-2020, to meet the new requirements in the 5G era, which was published in M. 2412 on October 2017 [3]. As the basis of GBSM models, a cluster can reflect path characteristics in the channels, and their variation over time can show channel time-variant and spatial-variant features in mobile scenarios. Many measurements in different scenarios were conducted, followed by theoretical analysis to study the statistical modelling of clusters. Reference [4] proposes the average number of clusters in indoor office environments, which can reflect the active number of clusters in different positions. Reference [5] shows the lifetimes of clusters in indoor-to-outdoor and outdoor-to-indoor environments, while Reference [6] reports them in an indoor hall environment. Moreover, how to continuously parameterize the clusters, and track and identify them in mobile channels have been considered in many studies. Reference [7] built an evolution model of the number of clusters from dynamic double-directional measurements in an indoor environment, and uses cluster-level parameters to describe the mobile channels. Reference [8] gives a Kuhn-Munkres-based method to model the evolution of clusters that is based on simulations of the 3 rd Generation Partnership Project (3 GPP) channel model. However, cluster-tracking statistical analysis to massive Multiple Input Multiple Output (MIMO) pedestrian mobile measurements, which can detect richer scatterers in environments, needs to be further studied. Although we performed cluster analysis in References [9; 10] with the K-power means (KPM) algorithm, it can only independently do clustering in each snapshot. The relationships of clusters between neighboring snapshots and their birth-death process are unknown, nor does the corresponding validation work on the primary module of the IMT-2020 channel model. Considering this, we focused on better presenting the evolution of clusters in mobile channels in both measurements and simulations. Thus, the contributions of this paper are summarized as follows. * The KPM-Kalman filter (KPMKF) algorithm is used for massive MIMO urban macro (UMa) pedestrian mobile scenario measurement. With this method, evolution including the birth and death rate and lifetime of clusters can be clearly shown. The difference of clustering results with and without Kalman filter (KF) part is also shown. * The spatial-consistency feature is introduced and implemented to complement the primary module of the IMT-2020 channel model. It makes the channel evolve smoothly without discontinuity when Transmitter (Tx)/Receiver (Rx) moves. With this feature, cluster characteristics are highly correlated among neighboring moving locations, which is closer to real conditions. In the meantime, comparisons among between measurements and simulations with and without this feature are shown. * Inspired by Reference [11], a novel clustering method based on the gradient boosted decision-tree (GBDT) algorithm is explored to train and validate the results from the above measurements and simulations.