论文标题
RIS辅助MMWave系统中的通道估计的变分学习算法
Variational Learning Algorithms For Channel Estimation in RIS-assisted mmWave Systems
论文作者
论文摘要
我们考虑了估计可重构智能表面（RIS）辅助毫米波（MMWave）系统中通道的问题。我们提出了两种基于RIS AID的无线系统中通道估计的基于变异的期望最大化（VEM）算法。第一种算法是一种结构化的基于磁场的稀疏贝叶斯学习（SM-SBL）算法，可利用双结构的稀疏性和通道元素元素的单个稀疏性。为了利用稀数度，我们提出了一个圆柱耦合的高斯先验。接下来，我们根据我们提出的先验设计了分解的基于场基的算法。该算法称为分解的平均场SBL（FM-SBL）算法，无需牺牲通道估计精度即可解决SM-SBL算法的时间复杂度。我们使用广泛的数值研究表明，I）提出的SM-SBL和FM-SBL算法的表现优于几种现有算法，而II）FM-SBL的时间复杂性较低。
We consider the problem of estimating the channel in reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) systems. We propose two variational expectation maximization (VEM) based algorithms for channel estimation in RIS-aided wireless systems. The first algorithm is a structured mean field-based sparse Bayesian learning (SM-SBL) algorithm that exploits the doubly-structured sparsity and the individual sparsity of the elements of the channel. To exploit the sparsities, we propose a column-wise coupled Gaussian prior. We next design the factorized mean field-based algorithm based on the prior we propose. This algorithm called the factorized mean field SBL (FM-SBL) algorithm, addresses the time complexities of the SM-SBL algorithm without sacrificing channel estimation accuracy. We show using extensive numerical investigations that the i) proposed SM-SBL and FM-SBL algorithms outperform several existing algorithms and ii) FM-SBL has lower time complexity than the SM-SBL algorithm.
