路新华^{1, 2},
MANCHóNCarles Navarro³,
王忠勇^1,,,
张传宗²
1.郑州大学信息工程学院 ??郑州 ??450001
2.南阳理工学院通信信号处理工程技术研究中心 南阳 473004
3.Department of Electronic Systems, Aalborg University, Aalborg 9220
基金项目:国家自然科学基金(61571402, 61501404, 61640003)

详细信息

作者简介:路新华：男，1980年生，讲师，博士生，研究方向为大规模MIMO、信道估计、变分贝叶斯推理和狄利克雷过程
MANCHóNCarles Navarro：男，副教授，研究方向为无线通信中的统计信号处理，包括联合信道估计和检测、稀疏信号估计和重构、多天线信号处理技术等
王忠勇：男，1965年生，教授，研究方向为通信系统及其信号处理、嵌入式系统等
张传宗：男，1982年生，副教授，研究方向为移动通信系统和接收机的设计、变分推理、因子图与消息传递算法

通讯作者:王忠勇　zywangzzu@gmail.com

中图分类号:TN92

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出版历程

收稿日期:2018-07-06
修回日期:2019-02-02
网络出版日期:2019-05-21
刊出日期:2020-02-19

Channel Estimation Algorithm Using Temporal-spatial Structure for Up-link of Massive MIMO Systems

Xinhua LU^{1, 2},
Carles Navarro MANCHóN³,
Zhongyong WANG^1,,,
Chuanzong ZHANG²
1. School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
2. Communication and Signal Processing RC, Nanyang Institute of Technology, Nanyang 473004, China
3. Department of Electronic Systems, Aalborg University, Aalborg 9220, Denmark
Funds:The National Natural Science Foundation of China (61571402, 61501404, 61640003)


摘要
摘要:针对大规模多入多出(MIMO)系统上行链路非平稳空间相关信道的估计问题，该文利用信道的时间-空间2维稀疏结构信息，应用狄利克雷过程(DP)和变分贝叶斯推理(VBI)，设计了一种低导频开销和计算复杂度的信道估计迭代算法，提高了信道估计精度。由于平稳空间相关信道难以适用于大规模MIMO系统，该文借助于狄利克雷过程构建了非平稳空间相关信道先验模型，可将具有空间关联的多个物理信道映射为具有相同时延结构的概率信道，并应用变分贝叶斯推理设计了低导频开销和计算复杂度的信道估计迭代算法。实验结果验证了所提算法的有效性，且具有对系统关键参数鲁棒性的优点。
关键词:大规模 MIMO/
非平稳信道/
时间-空间/
狄利克雷过程/
变分贝叶斯推理
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.
Key words:Massive Multi-Input Multi-Output (MIMO)/
Non-stationary channel/
Temporal-spatial/
Dirichlet Process (DP)/
Variational Bayesian Inference (VBI)



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