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Wideband MIMO Channel Estimation for Hybrid Beamforming Millimeter Wave Systems via Random Spatial Sampling

机译:混合波束成形毫米波系统通过随机空间采样的宽带MIMO信道估计

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Millimeter Wave (mmWave) massive Multiple Input Multiple Output (MIMO) systems realizing directive communication over large bandwidths via Hybrid analog and digital BeamForming (HBF) require reliable estimation of the wideband wireless channel. However, the hardware limitations with HBF architectures in conjunction with the short coherence time inherit in mmWave communication render this estimation a challenging task. In this paper, we develop a novel wideband channel estimation framework for mmWave massive MIMO systems with HBF reception. The proposed framework jointly exploits the low rank property and the available angular information to provide more accurate channel recovery, especially for short beam training intervals. We introduce a novel analog combining architecture that includes a random spatial sampling structure placed before the input of the analog received signals to the digital component of the HBF receiver. This architecture supports the proposed matrix-completion-based estimation approach in providing the sampling set of measurements for recovering the unknown channel matrix. The performance improvement of the proposed approach over representative state-of-the-art techniques is demonstrated through numerous computer simulation results.
机译:要通过混合模拟和数字波束成形(HBF)在大带宽上实现定向通信的毫米波(mmWave)大规模多输入多输出(MIMO)系统需要对宽带无线信道进行可靠的估计。但是,HBF体系结构的硬件局限性以及毫米波通信中短的相干时间继承性使得这种估算成为一项艰巨的任务。在本文中,我们为具有HBF接收功能的mmWave大规模MIMO系统开发了一种新颖的宽带信道估计框架。所提出的框架联合利用低秩属性和可用的角度信息来提供更准确的信道恢复,尤其是对于短波束训练间隔。我们介绍了一种新颖的模拟组合架构,其中包括在将模拟接收信号输入到HBF接收器的数字组件之前放置的随机空间采样结构。该架构在提供用于恢复未知信道矩阵的测量采样集时,支持所提出的基于矩阵完成的估计方法。通过大量的计算机仿真结果证明了所提出的方法相对于代表性的最新技术的性能改进。

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