一种基于实值变分贝叶斯推断的大规模MIMO系统下行信道估计方法

Downlink Channel Estimation for Massive MIMO System Based on Real?Valued Variational Bayesian Inference

酉矩阵变换是一种常用的实值化方法,可有效地降低计算复杂度。然而,在现有的基于酉矩阵变换的大规模多输入多输出系统(Multiple input multiple output,MIMO)下行信道估计方法中,观测矩阵的维度增加了一倍,若不进行维度压缩,降低计算复杂度的目标将难以实现。虽然利用信号空间和噪声空间的正交性可压缩维度,但信号空间只能近似计算获得,不可避免地带来性能损失。为了改善信道估计性能,本文将信号空间矩阵当作变量,在估计过程中自适应地调整信号空间矩阵,但这使得信号空间矩阵和稀疏信号矩阵高度耦合,传统的贝叶斯推断无法适用。为了应对该挑战,本文进一步引入列向量独立分解的贝叶斯变分假设,成功将信号空间矩阵和稀疏信号矩阵解耦。仿真结果表明,所提方法可显著提升信道估计性能。

Unitary matrix transformation is a commonly used real-valued method,which can effectively reduce computational complexity. However,the dimension of the observation matrix is doubled in the existing downlink channel estimation method for massive Multiple input multiple output(MIMO)systems based on unitary matrix transformation. Without dimensional compression,the goal of reducing computational complexity is difficult to achieve. Although the orthogonality of signal space and noise space can compress the dimension,the signal space can only be approximately calculated,leading to performance loss. To improve channel estimation performance,the signal space matrix is regarded as a variable and adaptively adjusted in the process. Since the signal space matrix and sparse signal matrix are highly coupled,the traditional Bayesian inference is not applicable. Therefore, the column-independent variational Bayesian inference(VBI)factorization is adopted to decouple the signal space matrix and sparse signal matrix successfully. Simulation results show that this method can significantly improve the channel estimation performance.