In this paper, we investigate the beam domain statistical channel state information (CSI) estimation for the two dimensional (2D) beam based statistical channel model (BSCM) in massive MIMO systems.The problem is to estimate the beam domain channel power matrices (BDCPMs) based on multiple receive pilot signals. A receive model shows the relation between the statistical property of the receive pilot signals and the BDCPMs is derived from the 2D-BSCM. On the basis of the receive model,we formulate an optimization problem with the Kullback-Leibler (KL) divergence. By solving the optimization problem, a novel method to estimate the statistical CSI without involving instantaneous CSI is proposed. The proposed method has much lower complexity than the MMV focal underdetermined system solver (M-FOCUSS) algorithm. We further reduce the complexity of the proposed method by utilizing the circulant structures of particular matrices in the algorithm. We also showed the generality of the proposed method by introducing another application. Simulations results show that the proposed method works well and bring significant performance gain when used in channel estimation.
翻译:在本文中,我们调查了大型MIMO系统中基于2维(D)波束的统计信道模型(BSCM)对两维(2D)波束基统计信道模型(BSCM)的估计。问题在于根据多个接收试点信号估算波束域信道动力矩阵(BDCPM),一个接收模型显示接收试点信号的统计属性与2D-BSCM生成的BDCPM的统计属性之间的关系。根据接收模型,我们针对Kullback-Leiber(KL)差异制定了一个优化问题。通过解决优化问题,提出了一种在不涉及瞬间 CSI的情况下估算统计科的新方法。拟议方法的复杂性远远低于MMVCFOCURS(M-FOCUS)未确定系统求解器(M-FOCUSS)算法。我们通过在算法中使用特定矩阵的螺旋结构来进一步降低拟议方法的复杂性。我们还通过引入另一种应用来展示了拟议方法的一般性。模拟结果表明,拟议的方法效果良好,并在频道估算时带来显著的绩效收益。