大规模MIMO系统中基于子空间跟踪的半盲信道估计

基于特征值分解(eigen value decomposition, EVD)或奇异值分解(singular valuedecomposition, SVD)的半盲信道估计算法需要进行重复的EVD或SVD计算,计算量较大,不适用于多小区多用户的大规模MIMO系统. 为此,针对多小区多用户大规模MIMO系统中的半盲信道估计给出了一种快速实现算法. 该算法主要利用最小二乘(least-squares)及线性最小均方误差(linear minimum mean-square error)原理导出一种新的递归计算模糊矩阵的方法,采用快速递归row-Householder 子空间跟踪算法对接收向量的信号子空间的估计进行加速. 仿真结果表明,所提出的算法估计性能良好,并能有效减轻导频污染的影响.

Subspace Tracking-Based Semi-blind Channel Estimation for Massive MIMO Systems

Semi-blind channel estimation based on eigenvalue decomposition (EVD) or singular value decomposition (SVD) requires repeated computation of EVD or SVD, both having high computational complexity. Such method is unsuitable for practical implementations in multicell multiuser massive MIMO (MU-massive-MIMO) systems. A fast implementation of the semi-blind channel estimation scheme for multicell MU-massive-MIMO systems is presented in this paper. This scheme provides a new recursive method to compute the ambiguity matrix, mainly using the least-squrares (LS) and linear minimum mean-square error (LMMSE) principles. A fast recursive row-Householder (FRRH) subspace tracking algorithm is used to speed up estimation of the signal subspace of received vectors. Numerical results show that the proposed scheme has good estimation performance and can effectively mitigate the effect of pilot contamination.