论文标题
MMWave MIMO混合界定系统的快速压缩通道估计
Fast Compressive Channel Estimation for MmWave MIMO Hybrid Beamforming Systems
论文作者
论文摘要
考虑到基于常规的一维（1-D）压缩感应（CS）框架，通道估计技术的计算复杂性高度，本研究提出了两种低复杂性通道估计策略。一个是两阶段的CS，它首先利用行组稀疏性来估算到达角度（AOA），并使用常规的1-D CS方法获得dementer骨角（AOD）。另一个是二维（2-D）CS，它利用二维词典来重建二维稀疏信号。为了对三个CS框架（即1-D，两阶段和2-D CS）进行有意义的比较，将正交匹配追踪（OMP）算法用作基本算法，并将其扩展到两个变体中的两个变体。分析和仿真表明，当比较1-D CS方法时，两个阶段CS的性能较低，但计算复杂性明显降低，而2-D CS不仅在性能方面与1-D CS相同，而且计算复杂性的略低于两阶段CS。
Given the high degree of computational complexity of the channel estimation technique based on the conventional one-dimensional (1-D) compressive sensing (CS) framework employed in the hybrid beamforming architecture, this study proposes two low-complexity channel estimation strategies. One is two-stage CS, which exploits row-group sparsity to estimate angle-of-arrival (AoA) first and uses the conventional 1-D CS method to obtain angle-of-departure (AoD). The other is two-dimensional (2-D) CS, which utilizes a 2-D dictionary to reconstruct the 2-D sparse signal. To conduct a meaningful comparison of the three CS frameworks, i.e., 1-D, two-stage and 2-D CS, the orthogonal match pursuit (OMP) algorithm is employed as the basic algorithm and is expanded to two variants for the proposed frameworks. Analysis and simulations demonstrate that when the 1-D CS method is compared, two-stage CS has somewhat lower performance but significantly lower computational complexity, while 2-D CS is not only the same as 1-D CS in terms of performance but also slightly lower in computational complexity than two-stage CS.
