【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。更多还原

【Abstract】 A novel tensor-based channel estimation algorithm is proposed for single-cell massive multipleinput 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.更多还原