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|
|Tidsskrift||Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology|
|Status||Udgivet - 1 feb. 2020|
Bibliografisk noteFunding 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 email@example.com 基金项目：*ⴑᨧ昰đ阎⩈?(61571402, 61501404, 61640003) Foundation Items: The National Natural Science Foundation of China (61571402, 61501404, 61640003)
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- Dirichlet Process (DP)
- Massive Multi-Input Multi-Output (MIMO)
- Non-stationary channel
- Variational Bayesian Inference (VBI)