Abstract
To deal with the estimation problem of non-stationary channel in massive Multiple-Input Multiple-Output (MIMO) up-link, the 2D channels' sparse structure information in temporal-spatial domain is used, to design an iterative channel estimation algorithm based on Dirichlet Process (DP) and Variational Bayesian Inference (VBI), which can improve the accuracy under a lower pilot overhead and computation complexity. On account of that the stationary channel models is not suitable for massive MIMO systems anymore, a non-stationary channel prior model utilizing Dirichlet Process is constructed, which can map the physical spatial correlation channels to a probabilistic channel with the same sparse temporal vector. By applying VBI technology, a channel estimation iteration algorithm with low pilot overhead and complexity is designed. Experiment results show the proposed channel method has a better performance on the estimation accuracy than the state-of-art method, meanwhile it works robustly against the dynamic system key parameters.
Bidragets oversatte titel | Channel Estimation Algorithm Using Temporal-spatial Structure for Up-link of Massive MIMO Systems |
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Originalsprog | Kinesisk (Traditional) |
Tidsskrift | Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology |
Vol/bind | 42 |
Udgave nummer | 2 |
Sider (fra-til) | 519-525 |
Antal sider | 7 |
ISSN | 1009-5896 |
DOI | |
Status | Udgivet - 1 feb. 2020 |
Bibliografisk note
Funding Information:收稿日期:2018-07-06;改*ผᔝ伂?2019-02-02;网络出版:2019-05-21 *通信作者: 王忠勇 zywangzzu@gmail.com 基金项目:*ⴑᨧ昰đ阎⩈?(61571402, 61501404, 61640003) Foundation Items: The National Natural Science Foundation of China (61571402, 61501404, 61640003)
Publisher Copyright:
© 2020, Science Press. All right reserved.
Emneord
- Dirichlet Process (DP)
- Massive Multi-Input Multi-Output (MIMO)
- Non-stationary channel
- Temporal-spatial
- Variational Bayesian Inference (VBI)