4.6 Article

Clustering-Based Nonstationary Massive MIMO Channel Modeling at 1.4725 GHz

Journal

APPLIED SCIENCES-BASEL
Volume 11, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/app11115083

Keywords

channel modeling; clustering algorithm; Massive MIMO; multipath cluster; non-WSSUS; radio channel

Funding

  1. Beijing Natural Science Foundation [L172030]
  2. NSFC Project [61931001]

Ask authors/readers for more resources

This paper investigates the radio propagation characteristics of Massive MIMO through realistic measurements and high-precision parameter estimation algorithm. New methods such as multipath clustering algorithm are proposed to bring deeper insight into the cluster evolution over the antenna array axis. The research results align with the theory of the cluster's visible region and aim to contribute to the radio channel modeling of the 5G Massive MIMO communication system.
The paper reports on the radio propagation characteristics of Massive MIMO. The realistic measurements are conducted in typical outdoor LOS and NLOS scenarios with the bandwidth of 100 MHz at the carrier frequency of 1.4725 GHz. In this paper the channel propagation in spectrum and space domains are investigated by employing the high-precision parameter estimation algorithm. Based on big data technology, we propose the multipath clustering algorithm and subinterval programming to bring deeper insight into the cluster evolution over the antenna array axis. The works focus on the correlation, and the result is in accordance with the theory of the cluster's visible region. Furthermore, a non-WSSUS (non-wide sense stationary uncorrelated scattering) channel analytical model is established. The whole research work aims to contribute the radio channel modeling of the 5G Massive MIMO communication system.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available