Name
Abstract | ||
|---|---|---|
This paper focuses on channel prediction techniques for massive multiple-input multiple-output (MIMO) systems. Previous channel predictors are based on theoretical channel models, which would be deviated from realistic channels. In this paper, we develop and compare a vector Kalman filter (VKF)-based channel predictor and a machine learning (ML)-based channel predictor using the realistic channels from the spatial channel model (SCM), which has been adopted in the 3GPP standard for years. First, we propose a low-complexity mobility estimator based on the spatial average using a large number of antennas in massive MIMO. The mobility estimate can be used to determine the complexity order of developed predictors. The VKF-based channel predictor developed in this paper exploits the autoregressive (AR) parameters estimated from the SCM channels based on the Yule-Walker equations. Then, the ML-based channel predictor using the linear minimum mean square error (LMMSE)-based noise pre-processed data is developed. Numerical results reveal that both channel predictors have substantial gain over the outdated channel in terms of the channel prediction accuracy and data rate. The ML-based predictor has larger overall computational complexity than the VKF-based predictor, but once trained, the operational complexity of ML-based predictor becomes smaller than that of VKF-based predictor. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCOMM.2020.3027882 | IEEE Transactions on Communications |
Keywords | DocType | Volume |
Massive MIMO,mobility estimation,channel prediction,autoregressive model,vector Kalman filter,machine learning | Journal | 69 |
Issue | ISSN | Citations |
1 | 0090-6778 | 5 |
PageRank | References | Authors |
0.41 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hwanjin Kim | 1 | 6 | 2.78 |
| Sucheol Kim | 2 | 5 | 1.08 |
| Hyeongtaek Lee | 3 | 7 | 3.15 |
| Chulhee Jang | 4 | 5 | 0.41 |
| Yongyun Choi | 5 | 5 | 0.74 |
| Junil Choi | 6 | 140 | 8.14 |