论文标题
智能反射表面（IRS）辅助杂种MIMO系统的感应矩阵完成和基于根部音乐的渠道估计
Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems
论文作者
论文摘要
本文研究了使用混合预言器和组合者的被动智能反射表面（IRS）中级联通道的估计 - 辅助多输入多输出（MIMO）系统。我们提出了一种逐渐估计通道参数的低复合解决方案。首先使用电感矩阵完成（IMC）估算出发器和接收器的出发角度（AOD）和到达的角度（AOA），然后是基于根 - 基于根部 - 基于根部 - 基于根部 - 基于根部的超级分辨率频谱估计的。向前靠背的空间平滑（FBSS）用于解决连贯性问题。使用估计的AOA和AOD，使用最小二乘方法（LS）方法估算了IRS的AOA和AOD之间的训练预码器和组合器，然后使用FBSS和Root-Music算法估算了AOA和AOD之间的角度差异。最后，使用小型词典的网格稀疏恢复估算了级联通道的复合路径收益。模拟结果表明，与最近报道的替代方案相比，提出的估计器可以在所有阶段使用低复杂性算法的知识来实现较低的复杂性和较低的复杂性。
This paper studies the estimation of cascaded channels in passive intelligent reflective surface (IRS)- aided multiple-input multiple-output (MIMO) systems employing hybrid precoders and combiners. We propose a low-complexity solution that estimates the channel parameters progressively. The angles of departure (AoDs) and angles of arrival (AoAs) at the transmitter and receiver, respectively, are first estimated using inductive matrix completion (IMC) followed by root-MUSIC based super-resolution spectrum estimation. Forward-backward spatial smoothing (FBSS) is applied to address the coherence issue. Using the estimated AoAs and AoDs, the training precoders and combiners are then optimized and the angle differences between the AoAs and AoDs at the IRS are estimated using the least squares (LS) method followed by FBSS and the root-MUSIC algorithm. Finally, the composite path gains of the cascaded channel are estimated using on-grid sparse recovery with a small-size dictionary. The simulation results suggest that the proposed estimator can achieve improved channel parameter estimation performance with lower complexity as compared to several recently reported alternatives, thanks to the exploitation of the knowledge of the array responses and low-rankness of the channel using low-complexity algorithms at all the stages.
