Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations
Abstract
:1. Introduction
- 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. It was proven to be an effective way to characterize cluster evolution in certain scenarios during linear movement.
2. Cluster Tracking in Mobile Measurements
2.1. Measurement Setup and Data Processing
2.1.1. K-Power Means Part
- Assign MPCs to cluster centroids and store indices:
- Recalculate positions of cluster centroids from the allocated MPCs to coincide with the clusters’ centers of gravity:
- If for all , then we go out of the iterations. Otherwise, we keep performing iterations up to the setting maximum iteration times.
2.1.2. Kalman Filter Part
2.2. Clustering and Tracking Results
3. Mobile Channel Simulations by IMT-2020 Channel Model
3.1. Simulation Generation Procedures
3.2. Simulation Results
4. Gradient Boosted Decision Tree-Based Model
4.1. Model Description
4.2. Numerical Results
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
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Antenna Type | ODA(Rx) | UPA(Tx) | |
---|---|---|---|
Number of antenna ports | 56 (16 ports were used) | 32 | |
Radiation pattern | Omnidirectional | Hemispherical | |
Inter element spacing | 41 mm | 41 mm | |
Number of elements | 28 | 16 | |
Polarized | |||
Angle range | Azimuth | ∼ | ∼ |
Elevation | ∼ | ∼70 | |
Center frequency | 3.5 GHz | ||
Bandwidth | 200 MHz | ||
PN sequence | 255 chips | ||
Speed of Rx | 1.4 m/s (pedestrian) |
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Wang, C.; Zhang, J.; Yu, G. Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations. Appl. Sci. 2019, 9, 886. https://doi.org/10.3390/app9050886
Wang C, Zhang J, Yu G. Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations. Applied Sciences. 2019; 9(5):886. https://doi.org/10.3390/app9050886
Chicago/Turabian StyleWang, Chao, Jianhua Zhang, and Guangzhong Yu. 2019. "Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations" Applied Sciences 9, no. 5: 886. https://doi.org/10.3390/app9050886
APA StyleWang, C., Zhang, J., & Yu, G. (2019). Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations. Applied Sciences, 9(5), 886. https://doi.org/10.3390/app9050886