# Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations
## 5 Conclusions  
In this paper, a series of cluster analyses of mobile UMa channels are presented, which were based on measurements and simulations. A UMa massive MIMO pedestrian mobile measurement at 3.5 GHz, which was performed with a virtual-measurement method, was described and analyzed. With the KPMKF algorithm for clustering and tracking, the spatial distributions of clusters in LoS and NLoS conditions were shown, respectively. From the figures, we could observe the cluster birth and death process and their spatial evolution during movement. The cluster birth and death histogram per snapshot was shown. The total snapshots of newly appeared clusters per snapshot was slightly larger than that of newly disappeared clusters per snapshot. The change of one or two clusters per snapshot takes more probabilities. In addition, the CDF of lifetime distances and VR diameters in Reference [9] were shown. In an LoS condition, most clusters showed shorter lifetime distances, which were less than 1 m.  
To get closer to the above channel conditions, channel simulations were implemented with the IMT-2020 channel model using the newly introduced spatial-consistency feature. With the simulation results, the evolution of generated clusters could be observed. They evolve smoothly when UT turns the direction. Additionally, comparisons between measurements and simulations with and without this feature were given by STD. The results showed that simulation with this feature was closer to the measured channels.  
The GBDT-based model, which is used for training and validating the above results from measurements and simulations, was introduced. We used the RMSE to check the accuracy of the model. Compared to the measurements, simulation RMSEs were smaller. Additionally, the GBDT-based model had better performance in an LoS condition than in an NLoS condition. Generally, it is a promising model for characterizing cluster evolution in linear mobile scenarios.  
Figure 8: Profiles of GBDT-based model. (**a**) AoD example of degree of fitting. (**b**) RMSE.  
**Author Contributions:** Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; software, [PERSON] and [PERSON]; validation, [PERSON], [PERSON], and [PERSON]; formal analysis, [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON]; resources, [PERSON] and [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], [PERSON], and [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON].  
**Funding:** The research was supported by the National Science and Technology Major Project of the Ministry of Science and Technology (Grant No. 2018 ZX03001031), the Key Program of the Beijing Municipal Natural Science Foundation (Grant No. L172030), and the project from the Beijing Municipal Science and Technology Commission (Grant No. Z181100003218007).  
**Acknowledgments:** The authors thank [PERSON], [PERSON], [PERSON], and [PERSON] for active discussions.  
**Conflicts of Interest:** The authors declare no conflict of interest.