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基于平行因子分解的大规模MIMO盲信道估计
赵凌霄,赵家乐,张建康,穆晓敏
0
(郑州大学 信息工程学院,郑州 450001)
摘要:
针对单小区大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统上行链路,提出了一种基于平行因子(Parallel Factor,PARAFAC)模型的信道估计方法。在基站端,将接收信号构造成PARAFAC模型,利用大规模MIMO系统中信道的渐近正交的性质,提出了一种基于约束二线性迭代最小二乘算法(Constrained Blinear Alternating Least Squares,CBALS),从而实现了盲信道估计。理论分析及仿真结果表明,所提方法与传统最小二乘方法相比,不仅提高了频带利用率而且具有更高的估计精度;与已有的二线性交替最小二乘方法(BALS)相比,所提算法有更快的收敛速度。
关键词:  大规模MIMO  盲信道估计  平行因子分解  约束交替最小二乘
DOI:
基金项目:国家自然科学基金资助项目(61571401,91438101);河南省科技攻关计划项目(152102310067);国家科技重大专项(2017ZX03001001-004)
Blind channel estimation based on PARAFAC decomposition for massive MIMO systems
ZHAO Lingxiao,ZHAO Jiale,ZHANG Jiankang,MU Xiaomin
(School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China)
Abstract:
A novel tensor-based channel estimation algorithm is proposed for single-cell massive multiple-input multiple-output(MIMO) uplink systems.At the base station(BS),the received signal is modeled using parallel factor(PARAFAC).A constrained bilinear alternating least squares(CBALS) blind channel estimation scheme is proposed,which utilizes asymptotic orthogonality characteristic of massive MIMO system.Numerical simulation results illustrate that the proposed scheme not only has a superior estimation accuracy than traditional least square method,but also improves the spectral efficiency.Furthermore,compared with the bilinear alternating least squares(BALS) algorithm,the proposed algorithm has a faster convergence speed.
Key words:  massive MIMO  blind channel estimation  parallel factor(PARAFAC) decomposition  constraint alternating least squares
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